{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:autoconf.conf:Pushing new config with path C:\\ProgramData\\Anaconda3\\lib\\site-packages\\autoarray\\config\n",
      "WARNING:autoconf.conf:No configuration found at path C:\\Users\\LeiZhang\\research\\config\n",
      "2022-01-18 17:11:22,319 - autoconf.conf - WARNING - Pushing new config with path C:\\ProgramData\\Anaconda3\\lib\\site-packages\\autofit\\config\n",
      "2022-01-18 17:11:22,442 - autoconf.conf - WARNING - No configuration found at path C:\\Users\\LeiZhang\\research\\config\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-01-18 17:11:22,598 - autoconf.conf - WARNING - Pushing new config with path C:\\ProgramData\\Anaconda3\\lib\\site-packages\\autogalaxy\\config\n",
      "2022-01-18 17:11:22,736 - autoconf.conf - WARNING - No configuration found at path C:\\Users\\LeiZhang\\research\\config\n",
      "2022-01-18 17:11:22,806 - autoconf.conf - WARNING - Pushing new config with path C:\\ProgramData\\Anaconda3\\lib\\site-packages\\autolens\\config\n",
      "2022-01-18 17:11:22,936 - autoconf.conf - WARNING - No configuration found at path C:\\Users\\LeiZhang\\research\\config\n"
     ]
    }
   ],
   "source": [
    "import autolens as al\n",
    "import autolens.plot as aplt\n",
    "import autofit as af\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from astropy.io import fits\n",
    "from os import path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''necessary parameters'''\n",
    "###source and lens redshift\n",
    "redshift_l = 1.7\n",
    "redshift_s = 2.5579\n",
    "\n",
    "###the position of the four point sources, to accelerate the fitting\n",
    "pixel_scales = 0.03\n",
    "positions_threshold = 0.5\n",
    "\n",
    "###mask size\n",
    "mask_radius = 1.1\n",
    "\n",
    "###the folder that output the results\n",
    "dataset_name = \"340GHz\"\n",
    "dataname = \"340GHz\"\n",
    "tid = 't5'\n",
    "testid = 'cl_para_pipe_t'+tid\n",
    "testid_non = 'cl_non_pipe_t'+tid\n",
    "path_prefix = path.join(\"howtolens\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''data path and the position of the four point sources, to accelerate the fitting''''\n",
    "dataset_path = path.join('./data/')\n",
    "\n",
    "positions = np.array([[-0.327,-0.0057*15],[-0.152,-0.0550*15],[0.723,-0.0295*15],[0.398,0.0283*15]])-np.array([[0.244,-0.0172*15]])\n",
    "positions = al.Grid2DIrregular(positions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''input the data image'''\n",
    "imaging = al.Imaging.from_fits(\n",
    "    image_path=path.join(dataset_path, \"340GHz_cut_f8.fits\"),\n",
    "    noise_map_path=path.join(dataset_path, \"340GHz_cut_n8.fits\"),\n",
    "    psf_path=path.join(dataset_path, \"340GHz_psf4.fits\"),\n",
    "    pixel_scales=pixel_scales,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-14 22:33:13,010 - autoarray.dataset.imaging - INFO - The image and noise map of the `Imaging` objected have been padded to the dimensions(4205,). This is because the blurring region around the mask (which defines wherePSF flux may be convolved into the masked region) extended beyond the edge of the image.This can be prevented by using a smaller mask, smaller PSF kernel size or manually paddingthe image and noise-map yourself.\n",
      "2021-12-14 22:33:13,012 - autoarray.dataset.imaging - INFO - IMAGING - Data masked, contains a total of 4205 image-pixels\n"
     ]
    },
    {
     "data": {
      "image/png": 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A+eFqMD9sDMaEYIeGCcF6rmzoPbGI9AC/w4cXrMeHN77HOfdQUOdc4FL8oMlk4Drn3OSo7DRgM3BzOGgiImcAnwRmOue2ish+zrnnk/rZ7mekkRm006x06oX1f8pcvHS8n+HxRWV/Rdnh8jdU2bcS6n6nQt0bEtper8q+puyf4MN8wM+0TUI7u9EV6hutohOfcBrdzRVcWXPbB/AzNkr5wA1AXwo7Tdt6bWfYEL//lTbg0paYHzYMw2g9DfLFJwDrnHOPA4jILcAs4KGgziz8oIgDlovICBE5wDn3TCSkPbrEev8OuMY5txWg0oAJ2O+M0TS2A0so6pBMBe6hqFNyRmDfgZ+BNROvUXIaeVaT55+Aa4E55DmOfKBpkmcs+UDTJB9omuQ5inygaZLnSPIDmiaQ53QKGibxuqeR52jyA7okZwXbWRy1nRZ9r5PJcwz5AU2T48kDeT4KfD36/i7azni8bMRJeM2UScoeD/wWr4uyHC+yb3oo7UjW4zeN7LEDBnRJ0uqHHI331rcDT+PjuH6Mv5E4J6pbjb1TtJ1619U2+EGU26P36fiBn1q++3Ep97PRPMwPG4ZhtJ4G+eJRwFOBvR4/m6RSnVHAMwnrPQI4VUSuBl4F/tk5d39SR2zQxGgSS8jx2UCHZA055gc6JatjNkAv/xaVnY8Xli+Iy48Fzo6Wz8bPzCjohUwLtjkVH9p2emSfjp/mVpjxMSWoexr+cgjr7owfzCF6D3VJzg7anhWtd2qw3TvwAyt+cMVrpXw9+j5TyLE00FJ5VNmnkmMZvdHMlzxvwvRQ2hN7wml0GlqXJI1+yKLovVDvrRQHGkjxPiHoTz3r6vdhqr9Qu3aK6aG0L+aHDcMwWs8QfPFIEVkR2Dc5524KVqvR2iLV1NEMA96If0L9duBWETnUJeiWtH3KYSMrTKMXH1veC8A/Jdq6TK+r/TktZsW/z/sr2O9T9lSM9qQwqp72ZRitYhxxfzRT2UllM0usq53R/Sv1far97u3+XbsZ88OGYRitZwi+eKNzblLwuilY7Xr8lPsCB+Enj5KyjmY9cLvz/AY/EXdkUgMbnDeGwHZ8SM1UCiEq8bS7Z1BM1fsFpfHx+Qo6JeHytWq7i4nP9Egiza2RrrtbhfIkoaV7Y1b8u/1XBfv7yu7FZxhcjh8QDUN5TgR+g0+XfB8wGwvlaR72hNNoB9KkAi7MvCj4l6dJpx8S0k9xVkc70q/sBVSvf1Jqv8zmSktt3IaYHzYMw2g9DfLF9wNjROQQ/E/zhcB7VZ35wCWR3slk4CXnXFJoDsA8/B/WpSJyBD6cYGNSA/udMYbAPeT43ECq3zznqLS7D8dSAeejl2csecaTH9A0mRbZM/BZ+Bz5AU2TU/G3oaH+Sbvzjuj9NGApeV6LdFTuAyaSZwz5QNMkbm8lzy8G9lWOX9DLL6J9+HsVyrOOHPcEoT9vwEJ5mofF0hvtQJpUwBui94J/OR8/5bRa/ZC30Dk6H4X+6YGOarRTDsbfhRX200QstXG7Yn7YMAyj9TTCFzvntonIJfg/gD3At51za0RkTlR+I/6P47nAOrwuw4cH+iTy33g9hpEish64wjn3LeDbwLdF5EHgNeCDSaE5YCmHjSHh6GN6QtrdeCrgCbG29xIPQUu6zPSMDp1+6/Uqy0rZIUOZaaK3u0XZYcacSunDXgP+dsDqI0xt/C36+Ehgf48+PmipjZtKMeXwGBH3nzWs4XxLdWnUkbSpgHtjbc1jlMIBVwX2XCy1cXtRTDlsfrgy5oc7hWam59X3uO3UBpo3FFpret5KmTBb2aaWNMVQ2764smvuiU3TxBgCS5iLv0kM0+4W7a8oO2RRMzrYoSyPWfF9+F/K/q6ylzSlh0aRYTW8DKOe9BP3E+HyghK2bmsMplRoT9J+DZdtnzYf88OGYRitJ8u+uJP6arSEULdkcfRZIc3uZPKcQ56L8bN6/kal3T0jSNF7d9R2erSeoYSQNOq0rTSzpFmcgv+OJwH3RWFNp1DQMPEpkwsaJu8gz7AgtfGW6LWUos7JlMiejumd1BebFm40hx3MpreknkYtqYA7KcSmVaQJ7YHBqY0po3/SD8xjLvbMqn6YHzYMw2g9WffFNmhiVCCuWwLQG4m65jkHH9l9R1RyDPG0u8MoDo7MCNZ5NnbqJdGDF9WFojYK0WfDgs/egReB1amNvxodn7XkWEQvX4rsMGWyYRidw+rENMFDTQVsDGYnGpnaeDW29w3DMAyjc7BHHUYFpiakAr5Y1T2zif0yPEmpjedY6uIGU1AKz+pURKNdGJepVMBZI21qYztC9cX8sGEYRuvJui+2QROjAkvKxm0PFtq9G6PZDE5tvIrC8bnR9E4azBBy0htGCvpNp6SNSat/YkeovpgfNgzDaD1Z98WdNMBjNIRQs2QJfubCvYEd6pZ8BTiKPGfjB0hOBI6kqJkxRa07VG+udFnUql6dlkZdnpUy/IToy07X1eU7J5SdjU8tfiZwV6R/chb++JxMnp0CzZnjgJ9SPMbHAitgINXxOzHNk3Q0KCe90YXM5UpWM1hDYxw+g9Z64poZazGdknahkv7JdOKaM+czj+OZV1Lv5Dig1zLvpML8sGEYRuvJui/O8nczqiKuWZJnDTkWBLbWLTkWf1lMxQ8UnBF9fgb2h7sV9FAMu5kWfF4Y9AqP3TZyLKY3ymqUZwo5ltLLNyL7jZjmSTqyLnplNI/VkKhb0he9wA+QmE5J+1BJ/+QB4ponoyh/nFc3ob9Zw/ywYRhG68m6L7bwnK5nqoq1vtR0SzKDPlYfVcf2/aZ5MkSyHr9pNI9xVNLBiNc1Ogd9vJKOsx3b9JgfNgzDaD1Z98Wd1FejIXjNkl4KsdbXKfsrqv5iirNLKoXUhOONleqmORWTQl+aSb36kRSOo8vTjOEuVvbX1bH9L2X/EJhcZl2Hp9hu95D1UXWjvlzBlWXLCrMRCtfjBvzMkuL1WaQfm1HSSWgFk9Dv6uM8jOTz5EoL3RmE+WHDMIzWk3VfbIMmXUGSbsm2SLPk4/gBkr8mzzHkOZOibsnRFHVLThu0dqNdKRyrKfhjN5E8R5LnVGAZcGykgfJ+4Hv4QSCHT2M8AX8rf0JkH4KFXw0m6/GbRj3ZwQOU1izpx3vZgu6F1i3ZCZiFHygxzZLOI9Q8WYX3G6X0TgrHPek8gR3YJOE45ocNwzBaT9Z9cZa/mzFAJd0SKOpeHEUxTKOUbonROfQQP3avUBxIOQ0/gLY0ekEO6OUH0TnxGDl+QS/fi+z9Mb2TwWR9VN2oJ6sTNUsWEde9CHVLJgRrsRkmnUeoeaKPpdY7gfh5oc8Tr3piZ0GI+WHDMIzWk3VfbI8ruoJKuiUhplnSPZwUs+LnxIWmd1IFjUqvJiIzROQREVknIpeVKBcR+XJU3i8iEyu1FZHeqO4qEfmZiBwYfT5aRF6JPl8lIjfWuj+MJMYlalnMHFTb6Ab0cS51XsR9sZ0ZmqynuTQMw+gEsu6LbaZJJqiUNnib0q74orJDQs0SSJcKuFlpg5O2o3VGmtWnSqRxC0kaJ2lSN+vvru1fxaz4OXGLsr8f9etE4Jf4KeIn4kN3LqSbQ3fq7URFpAefI3oaPtPs/SIy3zn3UFDtHGBM9JoM3ABMrtD28865udE2PgZ8GpgTre8x59zxdf4qXcgOZtNbMrTip8SvsaeJa1lsUGsy3ZLuQOudLCB+XujzZAJXcR6lQ3fmMZdufRZmN7OGYRitJ8u+OMvfrYuonDbYa1cUOIo8x0a6JXdFn52F1zA5pdmdN1pGQfT1JOA+8mwnz0nAcmA7eX4RnDNPkGMZvXxnYKCtNxKS7ebQHQF2rsWLJmsInwCsc849DiAit+AlLcJBk1nAzc45BywXkREicgAwulxb59yfgvZ74AVsjLqSHIIT+uHz8X9vC9oW5+BDcgp/gE23pDsI9U7CgZDCeXEwMJ/ieZPDQnc0DfLDhjFEanmGvluN29qrhjb71NBm3xra1NI3qG1f1HJR/6lylZK82KQ2tfBKje2G9qA56764Ox9JZI5K4TcfV/XPxs9CEfwD6WnR8lS6ecZA99EDnIw/9u8Ilk9i8ODZ+2LnlIXueERg2LD0L2CkiKwIXhcFqx0FPBXY66PPqKJOYlsRuVpEngLeh59pUuAQEekTkZ+LyKm17Q2jUghOyPH4v7cSvQ9Ttv04dwcFvZNy58Hxqr6F7gxmCH7YMAzDqBNZ98V2X5YJfNrgVRTTBGs7zt1N65nRqSxX9g9i51T8/FrSxH5lho3OuUnB66agTErU17NCytVJbOuc+6Rz7mDgB8Al0cfPAG9xzk0A/j/ghyKyd7VfxAjpj10bC9DXSljTMCpTKl3xKsqdX3ZWGYZhGEYj6KDxHSNOqGOi0wZfRJ6x5Ac0TU7GZ8UppBE+heIULD0Vq1lzpNKcepX6mKTj0Sr090var0PZF0nlaY9lOJX0dGA4/ly5D5+u+KgofGcZsI4878P/994/ei+kJ+4ejRMR2Ln+X3U9flZ+gYMYLHlRrs7wKtoC/BD/n+sK59xWYCuAc+63IvIYcASwYgjfoUuIa5hY2mCj3qRJV3w581jLvK7TOGmQHzYMwzBSkHVfbIMmHYvWMYFi2uCxFDUmzsL/edZphA0jiR58yA7R+zb84Bt4t7EsegG4rk1PXJiKWGfuB8aIyCF4HcgLgfeqOvOBSyLNksnAS865Z0Tkj+XaisgY59yjUfvzgYejz98EvOic2y4ih+LFZR+v+7fKJHENE0sbbNSboaQr7haNkwb5YcMwDCMFWffF2X8EkVm0jkm8zDAax0nK7t70xAXRq7SvJJxz2/ChM4vwExVudc6tEZE5IlLIdrMQP7CxDvgG8PdJbaM214jIgyLSjxc2KogdnQb0i8gDwI+AOc65ZqmVdTiDNUzipYbROCqlK+6WM7ARftgwDMNIR9Z9cQd11YizJCFt8BLiT/qTUs82MkVvvcJOKvVxKCEpafqYJuRmKNvRpNk31baD5NTGSfxC2To98X8Bbw/Kj65yvR2I0JBIJOfcQvzASPjZjcGyAy6utm30+bvK1L8NuG0o/e0mruDKgeUHiKcRtrTBRjOplK54NlfFzr8ruaJZXWsuDfLDhmEYRgoy7ott0KRjCDVMlgCnkGcmeS4FrsP/MZ0elZ3Rsl4a3cBJwI7o/Vf4FNaQ50LgFuBYfITJJLwsxhFk1osK5kW7jB34oIdxeK/8NHENE0sbbDQLna441NR5Gi9u5OiCc9H8sGEYRuvJuC/O8FfLGlrDZCb+udKCqPw4/NnaHVoSRisppComel+Kn31SmIGygxy/pJfvR+fqgWT2vMz4D4QxmNUQ0zHpi14Q1zCxGSZGown1TkppnIyieJ6ubnrvmoj5YcMwjNaTcV9smiYdg9YwuXRQuWG0hhOU/e7u0jgZVsPL6FjGoXUj4mWG0Sr0+Reep5k/N80PG4ZhtJ4M++IO6mq3sRM+1GYasBjYpnQjvqzqhzomlbQ2wvJmZdKpVi+jQFIfX0lol/b7hJeA7mMlzY8036lWXZJKbdNQ6fulIezzb1TZj9S5enO0rQymJM54/KYBc7lyIBynn+KT/FJ6UqZhYrQSrXGidc9mc2UsdKw3Kxon5ocNwzBaT8Z9sQ2atC1LyPFZevnsQDiO140oMBZ/e14YVDmtJb00DK9dAsVBkW3kuS84V58gx33ZTEmc8amIxuBwnA3Re+H8ngjk6ALdCKPtCTVOfgqxe4aJxNMRZypcx/ywYRhG68m4L7bwnLZlmpoC/v+p8nPwWUMles/w0J7R5vQAk/Hn4mSKeicF3pPdcJ3CD0RGpyIag8NxdFrhHP40GI/9oBqtpaBxIsB5qkynI85UuI75YcMwjNbTIF8sIjNE5BERWScil5UoFxH5clTeLyITg7Jvi8jzIvJgmXX/s4g4ERlZqR/2s9G2LFZTa784qNwPlhhGu6HDdXRKYp0Su8Ox8cpMUwjJsbTCRidRKR1x5m7+zA8bDSVtiDnAXjW02aeGNgAH1dDmrTW0OSR9k11r2AzU5qQ217KhP9XSCHi0hja1hNsnSRLUsw3Udp4r6uyLRaQHuB4fWrEeuF9E5jvnHgqqnQOMiV6TgRuid4DvAl/FawXodR8crffJavqSud/NzmZn4C584sod5MmR51+Aa4GL8bfm0/FJBc8ifviSLsR21S0J0boer5dZHmof9Cm/e7C8myrTtibsV6W65dqVsitpnIQk6a5U0mRJQ5o+6XCdieQR8rwP+D7wAvAscB8wm46+2834VMTuZAez6S2ZxtXSChudQlI64sJ5TKBxMo+5dOxcKfPDhmEYracxvvgEYJ1z7nEAEbkFmAWEgyazgJudcw5YLiIjROQA59wzzrl7RWR0mXV/EfgE8JNqOmI/M23FXeS4ml6ujrQfoBiRfBwwI1qeUaKtYbQLhXAdovf7gWXRC3L8gl6+FZ3jbyBTs06MDLA6pmGyiHgaV0srbHQCldIRQ1zjxKuc2BltGIZhNJ2RIrIisG9yzt0ULY8CngrK1lP8k0FCnVHAM+U2KCLnA0875x4Qkao62aGPFbLK9LKpLAvPhQyj8zgxZmVK38Ri6TPIuEQNk0xpQRhdgz5vtcZJR5/Z5ocNwzBaT+2+eKNzblLwukmtVeNKbLlSnWJlkd2BTwKfrvylitigSVuxiLnAKgansvTPOw2jE1kes+Ln+JKm96bu9NTwMtqY/tg5umBQqWF0HqU0TuK+uMPPbPPDhmEYraf+vng9cHBgH8Rgeblq6oQchhfoeUBEnojqrxSRNyd1xMbaW0qoYbIIOI08q8nzT8B/4tMKnwfcidepaYSGida5qKf+yVA0NJLaplmvrpukW6LLKl0er5RZLtU2zX5Nqqu/T9L302WVvk+oW1JJdyVJc0Zv95Ro2ycBy8jzB/L8DfAt/Ay7RcCZwN34UJ0Oupu1WPoMYBomRvappHFyOfNYy7zO1DgxP2wYhtF6GuOL7wfGiMghwNPAhcB7VZ35wCWR3slk4CXnXNnQHOfcamC/gh0NnExyzm1M6oj9zLQUrWGyGn/c50flx+PPwHNa1D/DqAc9FNMQD8MPjtwd2cPJcQe9XBtdAz10lMaJ3axnANMwMbJPpjVOzA8bhmG0ngb4YufcNhG5BH971gN82zm3RkTmROU3AguBc4F1wBbgwwNdEvlvYApeN2U9cIVz7lu19KVDHiNkFa1h8k+q3ARfjaxxkrIv7myNE4ulzwCmYWJ0H5nSODE/bBiG0Xoa5Iudcwudc0c45w5zzl0dfXZjNGCC81wclR/nnFsRtH2Pc+4A59zOzrmDSg2YOOdGV5plQnVdNeqHDlvwGia9FGKKv6jK76Qxs0zCw65TyTYyXCeJpPS4lcJm0qTZTVpXpbCfoeyLpNCXSiTti72VHZanCcfRbKlQt9a0yL9QZTeoa2AJHTXTBDoqmsjwXMGVA8sPQOwc1EGw/XTM83bDqJpSGid9FK+D2VwVO++v5Ipmda02zA8bhmG0ngz7Yhs0aSrb8X8KpwGLgSmBhsm1wCX42/MZFHVMDCNLnIQf2JkCLAXOIA/kuRT4Cl7/5C78jJMlwBm0tQe2aeEdyQ588ME4vFd+GtMwMbqLJI2Tp/GKeo4OuQbMDxuGYbSejPviDH+1dmQJOT5LL5+N9BvWENcwmUBxZonpmBhZpAc/EEL0fh/+GWcxR0mOBfTyuegagbaeeZLxH4isshpiOiZ90QtMw8ToDippnIyieH2sbnrvUmJ+2DAMo/Vk3BebpklTmWYaJoYRQ2uYXNpZGieCpbrsQMah9RviZYbRbejzPrw+2v6aMD9sGIbRejLuizM8HtQObAfuoRhq4JR+w+dV/SQNE62DkaQpkUabQ58CaTRO0qS01WVamyNJeyQpTTDEdT0qbScNOo2wtpPQ+zFJO6WSPkhYX2uY7KVsXR6izyH9fWrVbKl0HiSlSV6iyq5T18iXgH+gbUN1Mj6qnhXmcuVAOE4/xSfqxfOsiGmYGN2I1jiJ+2GYzZWxkLXedtI4MT9sGIbRejLuizP81dqBe8jxuSDUYGak31BgLP72fDo+mtg0TIxuoxCqUxhY3EaeOwauEZ+O+I72DtUxL9r26HCcDdF74TybCOToEP0Gw2gAocbJTyF2rzKReDritgzXaZAfFpF/BP4GL/GyGviwc+7VxmzNaDyVHiqWQz+gqob9a2hzeA1twF+laTcl6dscn74Jb66hDaTLN1DgiRrarEh62JjAxjE1NHq5SW3SPOQNqWWnKzJ8T2zhOQ1lqpoC/v+p8pn4kByJ3jN8phlGSXrwAyESvU+PlXZUqI7RtuhwHJ1WOIc/A8djP4pGd1LQOBHgPFWm0xG3fbhOnRCRUcDHgEnOuWPxP1gXtrZXhmEYRiuw+8OGsoS5wCrKpRRe1OT+GEa7Ew/XiV8/OpSnDch4/GZW6Cd+Li0oUW4YhqdUOuLw+mm766WxfngYsJuIDAN2Z3BWcsMwDAMyf09sUxvqzmbgXnxK1a3kmR6lU70e+ChwDMVQBB1qUKumRCWSpiPqbabVOKl2u0k6JAD7JNi7x4v2pLy9a4Uu6a8TTrLdpOuq7SauLEnDBOLfv9L0tyQNl0r7LemS1tP1dD+SpvPp9SZptFTSoElqezbeg04D7iDPg+T5OMV0xIuAM4G78ddPi71txuM3O5cdzKa3ZDpVSytsGMkkpSMuXD8EGifzmEtLn8HV7odHisiKwL7JOXdTwXDOPS0iXwCexP9A/sw597OhdNUwDCOzZPyeOMNfrVXcS44v0cuXAh2GwoyS4ygOlJyF7X7D0PTgB07AXx93RC+PT0d8bXRtFUJ7WkjGfyA6l9UxDZNFxNOpWlphwyhPpXTEENc48VIfLbySavfDG51zk8quVuSNwCzgEPxjlf8Rkb9yzn2/pq0ZhmFkmYzfE1t4Tt2ZUjaVpWkyGEYatDBym6YjHlbDqwIiMkNEHhGRdSJyWYlyEZEvR+X9IjKxUlsR6Y3qrhKRn4nIgUHZ5VH9R0Rkut5e5zEuUcOkWzQZDKMe6OtFa5y0xRXVAD+MH5X/vXPuj8651/GTbU6ud9cNwzAyQ2N8cVtggyZ1Z6nSYQhpQ00Gw2hbFiv7K+2ncdKA+E0R6cHH852Dj+d7j4gco6qdA4yJXhcBN1TR9vPOuXHOuePxk+A+HbU5Bi9uOBavSP21aD0dTL9pmBhGnaikcdLyK6pxcfRPAieKyO4iIviR+rV17r1hGEY2ME0TI5k/AvcBpwLLgJPIM408HwW+ARyJf1ixNKqTpINRLyrpkoRoTYk0uip6O3pdYYo2rb1xkLL3Ll+sq+r0ZSMTurRZ2ZuU/WyZ5VL2II2TcL/q/abtsG4azQ9drjVNtB22raRhknT8Kum5hHX1enTbSt+vHIUwnWn4AZRTybODPBfjxwUmAgvx+kFL8ZH2Tfa+jZmKeAKwzjn3OICI3IKfIv5QUGcWcLNzzgHLRWSEiBwAjC7X1jn3p6D9HvgUmoV13eKc2wr8XkTWRX34Vd2/WQOZy5Wsxj/z7gPWYxomhlEPkjROngbOZx7HM2/g2urliuZ2sEFTwp1zvxaRH+GjkLbhXctNya0MwzC6lIyH52T4qzWL+8jxNXr5WqSz8Cj+D17hKflY/Fl0Bh01nGYYLWcYxYGTs4GfEdc42UGORYF+0HCarnHSmB+IUcBTgb0emFxFnVGV2orI1cAHgJfwTqmwruUl1tVRrIaYjklf9ALTMDGMoVBJ42QUxetuddN7R0Nv1J1zV0CzR4EMwzA6kIwPmlh4zpA5VcX2flSVT2ludwwjs2iNkzntoXFS21TEkSKyInhdFKxRSmzFKbtcncS2zrlPOucOBn4AXJJie23POLTOQrzMMIz6oK+n8Lpr2bWW4SnhhmEYHUOGfXGGx4Oaxd3Mxd8s+NjeG1T5EuD0aDlNKEya1L66fj1TFyedIrqPSelwx8SLRqi2R6mmxyaUjVb2iGD5VVWmQ2zWKTv8erptpdCexNS5SWl4K112SWFOlUJfQiqlFK6UKjikUihW0nrStE1Ke3ynsm9U195iijNTmkRjsjasBw4O7IPwUSbV1BleRVuAH+KlCa6ocnttzyr8k+7i+VCkH5thYhj1QiuYhH64JU/iMv500zAMoyPIuC/O8FdrFUfgI35PBX4JvKO13TGMzHAWfkBwKn4wcivFdN4tojE/EPcDY0TkELxkwIXAe1Wd+cAlkWbJZOAl59wzIvLHcm1FZIxz7tGo/fnAw8G6figi/4nPJDoG+E3dv1WD0dNlJgI5TMPEMOpNqHHyU1VWatpaw8n4jbphGEZHkHFfnOGv1ixOpZdvDExNzTOF4m3D6eUaGYaRmmEUNUv8AEov1wXXng7faQIN+IFwzm0TkUvwI0I9wLedc2tEZE5UfiNeAfdc/NypLcCHk9pGq75GRI4EdgB/AArrWyMit+KFZrcBFzvnttf3WzWe8XhdhePx58OP8YfHZpgYRn0JNU7OY/B195NmdyjjN+qGYRgdQcZ9cYa/WrO4T4UILANOa2WHDKNL0KFxS2i6ECw0JB7TObcQPzASfnZjsOyAi6ttG33+roTtXQ1cXWt/24F+4uE5w7ABE8NoNKWuu5bQQXHxRr1IG8YOyWHISegMkNVwdPomw2pUBcrV0GZ2DW2mpA//33Pkpho2BJufHVm5kmZpE+e63aklCaphr8pVBtFhf9Uz7IubdiREZAZwHX53ftM5d40qn4J/QPH76KPbnXNXRWUjgG/ilS4c8NfOuRalw3wS+DVwEj4j5wTyTCHP+4H/At5GUVNE795KzqYdLoxKP0Jhuf7x2U/ZgY7Jm9V6tZqDjmIK7DdMiguTHLhLXG6hh+JD8efYP1b2xzVvIZGNwbLWP9k1uWly2t0ku9Jx1vt194SyNOmlK/VxtzLLldZVSRsl6ZzaUqFPLys7/H6nkueh9kpB3AFkxReHKYZ1GtQNwDmt6JRhdBlj8fnMwvTecGUsvXfTUxB3AFX44aOA7+B/2D7pnPtCUDaCNvHDhmEY3UJT/qWLSA/+X800vOjg/SIy3zn3kKq6zDlXarz0OuBO59xfiMhwBqthNpFfk+Mb9PKNKM3pFPwftKVR+ZGYjolhNINltDwFcYdNRcySL9YphhcRT4Maphk2DKMxrCF+3YEXRmpqCuJs+uEXgY9Rej5A2/hhwzCMATrMF6elWULnJwDrnHOPO+deA24BZlXTUET2xse7fAvAOfeac25TozpamZNUWsv3q/JTmtwfw+hWzlR2C1IQF34g0r5aR2Z8sU4xPLNEuWEYjUVfZzNpQQriDPph59zzzrn7UVOU280PG4ZhDNB5vjgVzRo0GQU8Fdjro880J4nIAyJyh4iMjT47FPgj8B0R6RORb4rIHqU2IiIXicgKEVkBL9X1CxT5FXPx6S29jsJ/qfL7GrRdwzDi3K3sG9W1uaQ53eisnPQt8MU6BKs+9EPseC8oUW4YRmPR19kC4tdl067DbPrhUrSVHzYMw4jRWb44Fc0a3ymlzOOUvRJ4q3Nus4icC8zDi2IMw8d0Xuqc+7WIXAdcRuF/UbhC524CbgIQOUKvv05MIs+jgYbJX0bdLGicnEb5M0BrO+jdn6T9UOlQvRIsa10LraUS2mk0TCCuV5GgYQJxHRMdsTRDbWX2n2L25H1/PbB8nJrgO4JNMftl9hxY/h1Hxsp+ffjwmP3SujfHN7xnsKw1TOp6daQ5tmnEzZKOdSUNnSTdEj3bN43+SRohLn0j96Kyk77fOcBw/AznxcCJ5BlGnkuBr9AUQebOm4rYAl98YEN8cSkthbdATEvBMIzGEqYfLqctNK/RncimHy5HW/lhwzCMATrPF6eiWTNN1gMHB/ZB+N/TAZxzf3LObY6WFwI7i8jIqO1651zhX/SP8D8YLWIFOZbSx0fIsRToA07Gnykn01FDZobR0fQAZ+OvvbOB+8ixgD5mkGMBcG/ju9B5UxEz44vX4LUT3hm9r8VrmBTSDDfrx80wuplC+uHCdbeW+HW5pnzT+pFBP1yhbdv4YcMwjAE6zxenoln3lfcDY0TkkEi06kJgflhBRN4sIhItnxD17QXn3LPAUyJSmEIwFdCihU1Ea5qc1LquGIYRMLU1miadNRUxM75Ya5qYholhtJ6WXJcZ9MPlaDc/bBiGMUCDfLGIzBCRR0RknYhcVqJcROTLUXm/iEwMyr4tIs+LyIOqzedF5OGo/o+jrGSJNGV8xzm3TUQuwc/a7AG+7ZxbIyJzovIbgb8A/k5EtuFjTS50zhWmE14K/CD6cXkc+HAz+l3k6WDZa5r0UpgL+WvisSdJqVgrhWGkORxJqWWTwnEqoUMttB3mqh8dLxqpvs+JwbLKw/GGC+P5fc/dZWHMPplfDiwfySOlOjrAE0E/XlY50HfbJR7+8VLSLta7NGkXA/H9WrFynXilQnma8BwdghMea51LXs0mDkOZ9qR8GQw+rTcHyxsrif4nnct6X/xMXZtLsOw5cTrdF1/BlQPLD+DjiArHexiWLccwWk0/g6/L8Lq9shHphzPoh0XkzcAKYG9gh4j8A3CMc+5PtPye2DAMowQN8MVVZhs7Bx9GPgaYDNwQvQN8F/gqcLNa9WLg8sgf/wdwOfCvSX1p2s9MNM17ofrsxmD5q/gvVartKmBSI/uXzHb84MhJwDbynEaeD+I1TU5oXbcMwwiYQp7V5PlH4Fr8QJbDD56cQcMeLXbQzTp0si/ewQMkaycYhtFaSmkNhdct7KAhk5yz54efxYftlGq7ipbeExuGYZSh/r54INsYgIgUso2FgyazgJujB3zLRWSEiBzgnHvGOXeviIzWK3XO/Swwl+MfGCbSYT8zreLX5PgGvXyDuUAeKOolHMlglVPDMJrPUvzV6a/QHAvp5T+Da7YBs0467AlnZ7OabcAF+CfZi6L3lVHpW7CZJobRatYQvy7Ba5tcMPDZaup+pZofNgzDaD21++KRPsvXADdFQtZQOtvYZOKUy0j2TJXb/2vg/1WqZFp5VaF1TEJOaXZnDMMoybSY1RR9k86Lpe9gxsWO6cxBpYZhtBp9Hc5E++IGXKnmhw3DMFpP7b54o3NuUvC6Sa1Vo7OB1ZyRTEQ+iZ+a/oNKdW1sviq0jknIcuKpTXcus1zKrmeq2TSaGuF20miYQGy26J5Kj+J4VXVKsMVcPKXw9F0Wxexz47NUmURxwHE/nouVbWJEzN7IvgPL29U+fWWr6uMm1cdQX+NVVabtQYT7XGtvpDkeleqm0aQJ16XPL60fonVLgvIRqmhkgq3rak0T/fU2BcvrVdkTuo/7Kjtcmb5ebo9Z8Wt2MT7DTp2xJ5xNZFXsmD6tSvuxmSaG0Wr6lZ3H5xgs+uJVwIT6btT8sGEYRutpjC+uJttYTRnJROSDeNXNqYF2X1nsZ6YmpuBn8tyHzTQxjHahMNNkBnAN8JPmbNa8aJOIP0g4GHgrRa2E41rQI8Mw4hSuw8J1uSN6FSn1QLAOmB82DMNoPfX3xQPZxvDPyy4E3qvqzAcuifROJgMvOecSQ3NEZAZe+PV059yWpLoF7GemKk6ll28MTC/N89f4H/53YHM8DaNdGEZRDvQyevlJcM1OK9vK6BTGx47pjyn+/bIZJobRHuxE8Xocj58ffQEEvtiuVqMUaWZaF9Azo6tBz56ulqPTNxlWQyja36RvAsA/VxWJEGPWYbekbjOZ36Ru01NjdslVh6WfkfajERW1PAfx+sN7p24D+ECDtGyq5ZztbqrM+rgQOBdYB2whyCgmIv+Nn+0wUkTWA1c4576FT3iwC7BYRACWO+fmJPXFBk2q4j411f8+TPzVMNqZO5uTfrgQv2k0gf7YMbUUw4bR/ugUxA0JpDM/bBiG0Xoa5IuryDbmgIvLtH1Pmc8PT9sPGzQpyR/xAyOnAsuAE8mzjjwfAb4DnA4Mj+om6Zbo3TsUDZOhkNRHPeqpR1z3L1+uTzedAC8YVzp531/Gik4mbk+gL2Yf9tIfKMczb4jrXjwf9PGpWEgbvPTEm+ONtYbGxmB5kyrbrOxB2iKvlFkuVTcNQ2mbpKmjj7XSDwl1SkarqtoOEyGqXTxI00Tvx2cT6uoHEuv1+RjumxcTyqaR57dR+uEv4mfrLQLOBO7GD6DUwbNbLH0D2cFsei3FsGF0MDoF8eXMYy3zBq7recxlyDkJzA8bhmG0noz74gx/taFwHzm+Ri9fi9KVTsU/qV4SlR+FHzgxDKM9WUKYfhi2k+MOerk2uqZ7qMvMk4z/QLQWSzFsGJ1OU1IQmx82DMNoPRn3xZZyuCSnqhR5H1Hlp2EYRjtzrrIvblwKYkt12SAsxbBhdDpNS0FsftgwDKP1ZNgX26BJSZYxF58Yz8fgfkuV39vk/hiGkY6Fyr5eXdNLqAuFUfW0L6MK+mPHbMGgUsMw2h19nS4A5YvrcCWbHzYMw2g9GffFHdTVZvIO8jxKno8C3wA+AByDn2FyX/ReGBrTuhHhLq2nZkkanYtK2w21LSppmOwbN0cHy0epqsfGzTe97cmB5cNZFys7mKdi9p68HLNf27U4nrdxl3gfHuKYmN3H8QPLq7erxKOrVB/XKTvUONmoygYJfv8pwdbHJ41aeKVjG66rki5OkqaJOtYjVXGoUaOO5SA7PPYHqTLdxU3KfpjyaP0TbW8Kle/1Pg7342zgNWA6PrBjYhSsczFwPXWbLZbxqYitZSwbmDeghTAdH5JjKYYNo3PQKYi1NpFXPRki5ocNwzBaT8Z9sc00KcmvyLGYPv6SHIuB3+A1TAQ4g46aS2QYXckwYAb+mp0B/JIcd9BHjhx3ULfZYhkfVW8tazgQeCdeA2EtXvlAonf78TKM9qeQgrhw3a6F2HXtVU+GiPlhwzCM1pNxX2z3nSU5TcXcmoaJYXQ2Z5qmSccR1zQxDRPD6HzGYZomhmEYmSXDvriDxncayVZgKcV0pFuZi/9B9zG3yyj+yWpWmmCN3q4OTUg6lLrtXmWWAfaLmzqEY3SwrFMOj46bI4N4l71U+M3WgZTNnmeiZ04D9i5Fe53a0DJOjdlLOWNg+cW7RsU7sUL1MSk8Z5MqSwzHgXia4TThU5VCd5LWVWk7CcdWp/fVYTVhCM47VNk7XMx862GPDCwfGE2yLscGdWz/MCKI7XlVVd5Uwd4sxeVt+tzVxyc87+9S1/Q8/DVzGn7WybnU5LkzPhWx2czlSlbj/0b9FGLHbCdgQgv7ZhjG0FmFz5pTuK4ncBXnUQy56+WK9Cs1P2wYhtF6Mu6LbaYJAEvJcS19zCTHtcAj5PE36PkKLQ3D6Azi1/Sj5PgKfbybHF8B7qltpRmfithsVsNAmuFRxI+ZJLQzDKMzEOLX9Sj89b4Nf/3XvFLzw4ZhGK0l477YBk2AwVP3/35QuWEYncxZyr6ofuE6GZ6K2GwGT90vMr7pvTEMo97o67huIXjmhw3DMFpPhn2xDZoAcLdKgXejKl/a3O4YhlFn7lb2N+qTgrhBo+oiMkNEHhGRdSJyWYlyEZEvR+X9IjKxUlsR+byIPBzV/7GIjIg+Hy0ir4jIquilHWDT6EenI42XGYbR2ejrOLzea77GM/500zAMoyPIuC/uoK42klPJ81CQjvRv8AIPBY2TKVQ/FJYm1awmjV5KmkO3m7LD1LMqxfCuahL8mylva72TEeV78L+q8DGlU/K86sfGINXxWpVi+D5OjtnPLjq0aNylNqw1TXS622dDY4sqfEHZLyr7FepDmnNGH3d9zuxeZpnBx1Jr0kwKls+Ki428Y9SymH08fQPL+/N8rEzr1TzBITF7+JFbB5Yf3aSeOz5Lsh2mhd6oz2tth/vqLPw1PBU/QDKRPDuRZw5+kLR9xJ5FpAfviKbhVXfuF5H5zrmHgmrnAGOi12TgBmByhbaLgcudc9tE5D+Ay4F/jdb3mHPu+MZ/u2TGAnfg05E+DZyHn8ZvKYYNIxuEKYhX4tMOF9IPn4NXmzK6kVr+jujf/GoYXUMbgLelb/JX6Zvs/G9am606Pr3vVanb/C1fT93mTSs2p26jZO2q5vYDz0nd5pF9j0jdZuWbtYhflWidwKoYyn9Eo9XYTBMAlql0pL/E/7mS6L2D5g4ZhlGCHvzAiUTvy8mxiD4uIMciak5B3JhR9ROAdc65x51zrwG3ALNUnVnAzc6zHBghIgcktXXO/cw5V/jFXs5gKeCWs4ZiOtJR+KNmKYYNIzuEKYiHEU8/XHPy4Yw/3TQMw+gIMu6L7T4UGKxpYhomhpFtptRH06T2H4iRIrIieF0UrHUU8FRgr48+o4o61bQF+Gv8pI4Ch4hIn4j8XEROLVG/KWhNE0szbBjZpW7Xe8Zv1A3DMDqCjPviDupqI7lbpSO9myEJQxqG0eYsVdf8EgaLxVaHq20i2kbn3KQyZaUSxbgq61RsKyKfxM8R/UH00TPAW5xzL4jI24B5IjLWOVfbPOEh0E88HekwTADWMLJKqeu9Vmr0w4ZhGEYdybIvtkETYLCmiX7Q+nqKdaXRJUlCHxodB5e0HV22l7L3CZaV7oXWKdE6GGH5nqpMdTHUMfkdR8bKtIbJdhUCtSEIgnzwaaVm8AsVSPiLYFlrmKxT9kZlE/4vfC6hDOBlZSedF2nOgzTnlz4vtB3E+Op4S30sj1V2ENZ5+qilsaLpLIrZk4IdvZ/aby+r8221UqN4LdA8eebYeLDr5offFO+TPn4jguWNenxA7/OdE8pOIc/aIWuaOIHt9fei64GDA/sgfMh/NXWGJ7UVkQ8COWCqc84BOOe2Aluj5d+KyGPAEQy+mhpOqGlS0DgwDCOblLre59Wwngb5YcMwDCMFWffFFp4DDNY0WVaxhWEYncx9ddM02T4s/asC9wNjROQQERkOXAjMV3XmAx+IsuicCLzknHsmqa2IzMALv57vnBtQPRaRN0UCsojIoXhx2cdr2yFDI9Q0GZLGgWEYbU/drvfG+GHDMAwjDRn3xR3U1UZyJr1cOxBXmzdNE8PIOFPo5UvBNV9bOJ4T2NZTy9jzjvLr9NltLgEW4bVQv+2cWyMic6LyG4GFwLn4uThbgA8ntY1W/VVgF2CxiAAsd87NwU+zuUpEtgHbgTnOOZ0qqimMAy6gqHHw41Z0wjCMplDqep9Xw3oa4YcNwzCMdGTdF3fpoIkOddGaJouBM2pcd71CNjRpwnWSUgxDLDxnhCrSIRy6PAz50F1QoS/PPlZMNbtppF5RnFc3qRCih4Pvs0pV1vaDwbIO53hVS0E8r+wwrbAOv9HhOWlSDOudMyyhrBLh8dTnkD7WwX4coYp0rpSj4uahY4vP+U7ml7Gys1Qu57dvCHa63qX7xc3dD4zvtzA066E94umkHzxIheeMUOsOQ8IGXRJp3Nkydc3fQfyary6VoRNh+7Ba3Ohryet1biF+YCT87MZg2QEXV9s2+vzwMvVvA26r2OUGcAVXxuwHME0Tw+gWSmmaaJ9wJVdUXE+j/LBhGIZRPVn3xV0anrMdL/zooveTyXMOE8iT5xzglJb2zjCMRnNqcM1Pxw9kOeAevH+onu09PalfhmcHfqDERe9H47UNChoHY1vXNcMwGsxY4tf70cT9QZpnj+aHDcMwWk+WfXGXzjRZSo5r6eVa5gJ5HsI/aS5k4DyS2meaGIbR/iwjvOZzLKKXr0b+AHzkS2UcMkjE2Kie1fjhqgvwT5wXRe8ro/K3YDNNDCOrrCF+vYPXNin4g9VVrsf8sGEYRuvJui/u0pkmZ9JLMY528Cz3Kc3tjmEYTSauWxT3B1OqXotD2EZP6pfhGUd8388sUW4YRjbR1/dM4v6g2uvf/LBhGEbrybov7tKZJncpPYPrVPlSap9pEmpOVNq9Wp8izeFIqltB02RYkKpVpxgeoWydVjjc7KuqbL2yNxW38+qwfeJlmyu0fSJYfliVafvZ0HhBFWo9yySdEq1pskXZSVoklY5dvdJW6+3ousGxHaGKKmiaHEf/wPJkfh0re/tTD8YrhxmI9S4+OG4ed+7amP3QG4o6JgeqTLoPVjofdRrlmrk7ZsX9wd34rLzVsb1b3WgdWEVc0+BpVd6PzTQxjKzSr+w80EfRH6R5qmd+2DAMo/Vk2Rdn95ul4kh8dO0U/IDJaa3sjGEYDWdK9H4m8GXio0DVk/WpiI1GlH0w8Fb8E+Z+4Lim98gwjGZRuL4L1/sO4jom2j+Uw/ywYRhG68m6L+7SQZOz6OWLQbrRsyj+PJuWiWFknx4YSDP8MXpZZCnHW8B4BqcclaDMMIzsshPF63w8XgBW+4OftKRnxtCoJVNkLW32q1xlEEfX0Ibaovb/IX2TT+97VQ0bgk+tujZ9o3+vYUNP1tCmOom4QRz36WpVjYqMYFNtG6sFPdu+KtJk4SyQNuMmpJvdXo923UGXapr4FMOrKEzHX9rCvhiG0Vpq9weFUfW0L8PTD7F9r6frG4bRPdTqD8wPG4ZhtJ5G+WIRmSEij4jIOhG5rES5iMiXo/J+EZkYlH1bRJ4XkQdVm31EZLGIPBq9v7FSP7p0psmJ5JlOnjnAjaRLMVxpNDzcpUPRLNFtK43+hfUraJqMLLMMlTVNQjYpW+uUbEso26jsJE2TJ1TZNq018lywrAU29Khukq3L9D4fiqZJSNonKknnlDrWYdURquqb4+aeh/8xZh/OYwPLx+nb1V+odYWSJ1pGRtk7Hxq3D5781MDyvrryCLXPd1XftyEe61TyPESei4HrSftIyW6+a2csPn9RIeXoOa3tjmEYLaSUP5hXZVvzw4ZhGK2n3r5YRHrwN+fT8P8W7xeR+c65h4Jq5wBjotdk4IboHeC7wFeBm9WqLwOWOOeuiQZiLgP+NakvXTrT5D5yLKKPC8ixCLiv1R0yDKNlLCPHHfSRI8cd+HTE1ZF1pfBGswafYvSd0fua1nbHMIwWUqs/aKQfFpERIvIjEXlYRNaKyEm1fTvDMIxs0yBffAKwzjn3uHPuNeAWYJaqMwu42XmWAyNE5AAA59y9DH6iXmjzvWj5e8DsSh3p0kGTKTWnGDUMI2voFOTVa5r4qYjDUr8Mj045bCmGDaN7qdUfNNgPXwfc6Zw7Ci+9srZCfcMwjK5kCL54pIisCF4XBasdBTwV2Oujz0hZR7O/c+4ZgOi9okhSl969L1UpRpdiArCG0a3cXSLl8PSqW9u08NrpJ55yeBgmAGsY3Uopf1AtjfDDIrI3Pp3ihwCip5yv1X1DhmEYGaFGX7zROTepTFmpRGquhjpDpksHTU4hz9pA02QKyToTtepV6HZaa6QWtfByhOtWGia6GyOC5UqaJrsqO5T12KTKtJJ0WK41TJ5VttY02ZRU+JyyXw6WK+mSJOmU6LI0Ktd6J2v9k7BcbyfNeaC3o9qGx0vr0ahjvd8ez8fsA9kwsHzIi+oAParWFYqav1SimyFKbX3E5P8dWN4rduxgp13j96M79PfbVma55AdJOkDhfjyDPI+Q5x+BLwJnJ7SLk/X0ao3GNE0MwyhQq6bJEPzwSBFZEdg3OeduCuxDgT8C3xGR8cBvgY875/5cy8YMwzCyTIPuidcDBwf2QRD8Yam+juY5ETnAOfdMFMrzfIX63RqeozVNqtcwMAwja9xLjjx9TCVHnnTZczBNkyFgmiaGYRSoXdOkZj+80Tk3KXjdpFY9DJgI3OCcmwD8GS8WaBiGYSgadE98PzBGRA4RkeHAhcB8VWc+8IEoi86JwEuF0JsE5gMfjJY/SBUZ7rt00ERrmlSvYWAYRtaYqvzBtBRtTdNkKJimiWEYBWr3Bw3zw+uB9c65Qr64H+EHUQzDMIxB1N8XO+e2AZcAi/CaUrc659aIyBwRmRNVWwg8DqwDvgH8/UCPRP4b+BVwpIisF5GPREXXANNE5FH8jf81lb5dF929bwbuxYfi3KA0DO4hPiV/KKE6SeE5lVIQJ6W0TdoOJIbnjFBVRyaUaTupi5XSCIcRHjrCRtvoNMJPB8tJ4Ti6bZp9CPEQjkptk0Ju6knSeVPhHApNHZ6jbB0aMyKIiRKdRlhPcgvs1/8UL9p5X1VXaVbvHoQ99bCdRPQuTzxE+hxKqhxeLwuVP1iE35HTo+V3QJmRcAvPScsOZtPLOIr6BaGGwU7AhBb2zjCM1rGKuD8AmM2VA/5i3oCXiNMoP+yce1ZEnhKRI51zjwBTgYcqtTMMw+hGGuiLF+IHRsLPbgyWHXBxmbbvKfP5C3ifXjVdNNPkXnJ8iT5mk2MRefzNeb7V3TIMo+XE/cEaclxNH6eR42r8oGp5ttOT+tW9rGYbcAF+SGsD8X1fSsnLMIzuQIj7gw0Q8xdxMa04DfTDlwI/EJF+/CSYf6/x6xmGYWSeLN8TVxw0EZGdRGSCiMwUkTNFZP9mdKz+6JCckDTT8Q3DyBZafvRflK8oPxBdGFVvxg9ENnzxuNi+nalKLXOOYXQv+vqfCcoXlw7YaaQfds6tivROxjnnZgMvdb4fNgzDqD/NvCduBWXjC0TkMOBfgbPwuTP+iM/NcYSIbAG+DnzPObejGR0dOjrNcMgS/Nc0DKP7WKTsa5WvaK1/yJYv7o/tWx311Y8NnBhGt9Kv7AVAH6Evbp2HyJYfNgzDMNKSJMrwb8ANwN9GsUIDiMh+wHuB9wPfa1z36smUKK3oxcD1wNF4zYIl+CfN4a4YSirgpPVU0sAIy7UeQ5KGCcR0TPRmdFrhkQllWgdDs7nMMgzWNFlfZhkAJYQxqEKY+UkLbKRJBVyJpJTDmkoaNa1A9WFYmeUSdo86x4YTpPvdqtom2K+oVNM7p5CV0SPMO14dHq+g01hrO0a1Giaac4DhwLn4kMnTyTOMPJfhdaFOK9vSIc3IhpMhXzyWDcwbSCk6HXgLDGgWHNfKrhmG0VIK13/BHxyNH9Iu+AuflHgw5ocNwzBaT5N8ccso+y++nHBKVPY88KVGdKhxLCPHHfRyB3OBPMfhI2jPoqv0cA3DUAyjGCgyE1hAjtvp5fbIVxxO0kyTRmfDyZYvXjOQUrQPL4NeeG5sM0wMo7vZibg/eABi/mIlayjnKcwPtxO1HIu9amhzUPomI2qMppqdvsm7xn8/dZtPvnht+g2Bzy2SkjvuS99mn/RNmDymhkYMfqhWDS8MehJcBYMe6lbJploa6QfG1VDLg+K0STHqR5YzRFajaXKxiIwI7DeKyN8nNGlTzqxap8AwjG7m3HbVNMmALx5nKYYNw6gKnYK4FZommmz4YcMwjPqTdU2TarLnfNQ5t6lgOOf+F/how3rUMO5mLj6lXVGnwDAMQ7Owal/R5B+IDPji/ti+1RoGhmEYBfpB+eLSHsP8sGEYRuvJ+qBJNXNodhIRKcRwikgPXgCgwziDPA+T51Lga8DZwC5RWVrtkSTCdaXVwAg1NSq13bu8rWenvTnBrqRpojUkNlVZBkrjZIsqfE7Zzys71DHRU9OSpp114rQwfWyTzscU36/C7Dw9he618LLeRVXW9h7Fxb30dvZQtjpVNzGi5LL/QH13rZsTs9Nq0IQk7eMcefrJ8yl8GHt5TROgmfGbGfDFcU0TnbfIMAyjwFjgDiprmoD5YcMwjHYgy5om1cw0WQTcKiJTReRM4L+BOxvbrUZwLzkW0McMcswH7ml1hwzDaEt+Ro7b6GMCOW4D7i1b04+qD0v9qpEM+OKipsmBwJoW98YwjPZlDcT8RTmPYX7YMAyj9TTZFzedagZN/hW4G/g74GL8XPVPNLJTjWGqio2d3srOGIbRtpzXlpomZMIXm6aJYRjV0Y6aJmTCDxuGYdSfrg/Pcc7tEJHvAnc75x5pfJcaxVLm4n94izoF1U4OD+MPKoXNDCV1cVJ9HY6jNKzDsBodjqMFxkNb19Vfb1NCl3R4jg6liJW/rApfVLZWlA5DcuqpAj2UdaU51mlCQ+rItjLLMOh4bVFpeF8O1OvdvvG6ogXnDwzK3qDKDk22NwSNn0Ot+FnVVqexThWek7Sfk47PT0v4iqTsOfV3+CIyA7gO6AG+6Zy7JvTFwO+i8kdEZAvwIefcynJto88/D5wHvAY8Bny4EJsvIpcDHwG2Ax9zzi2q+5cCCpomhX07DMuaYxhGafqBlYS+uJ/y2XOac+OdnXtiwzCM+tNJgyBpqSZ7zvl4Ha47I/t4EZnf4H41gCnkyTGBJeSZDZzR6g4ZhtGWnE2edzGBPvK8iyRNk0JO+rSvJKIY+evxo7rHAO8RkWOULz4HmAg8BFwE3JDUNlr1YuBY59w4/KDL5VGbY4AL8YIBM4CvRetpAGPZQFGjoLxCgWEY3c5YiPmLch6jEX64HNm5JzYMw6gvzfTFraCa8JwrgBOI5hw451YBoxvWo4axlBx5+phKjnmYpolhGKVpuabJCcA659zjzrnXgFuAWcR98Sz84Mho59xyYISIHJDQFufcz5xzhflHyynOOZsF3OKc2+qc+z2wLlpPAzBNE8MwqqNNNU0yck9sGIZRX0zTBLY5515qeE8azjQVGzujlZ0xDKNtqV7TBKg1fnOkiKwIXhcFqxwFPBXY66PPQl9crk65zzV/jU9MkbSuBmCaJoZhVEe1miZQsx+uhYzcExuGYdSfrtY0AR4UkfcCPSIyBvgY8MvGdqsRLFY6BfOBc8vUraSVkES9Rsx2U/ZeylZ6DaE2yWhVVduhpskIVaZ1MDYpO9TFSEwHC/H9mKRZousOhbSaJfXabqtQ/d8WnBcVUkJv4o0xeyNFIZMN+8Q1c0aNURo0YcborWo7x8fNPx8fH5t9hCMGlp/QJ+d6ta5ETZM0x7qSFlG4H+cpX7EYn6J8MAXRqxrY6JybVKZMSm4q8MX4xM7/QNwXu4S2xZWLfBK/835QYXsNYFVs3+4ETGjMhgzD6HBWoTVNVlHKYwzBD9dCRu6JDcMw6kuTfXHTqWamyaX4QNKt+NRqf8LfrBuGYRj1Zz1wcGAfhA/pD33xsfjBk39Qdcq1BUBEPgjkgPc55woDI4lt6oskWIZhGEUG+4e28Bh2T2wYhtGFVBw0cc5tcc590jn3dmAy8B/OOf0MuwM4R03zrDZzjmEY3cVs5Sumla3ZoPRq9wNjROQQERmOF2mdr3zxX+Nnm2wVkROBl5xzz5RrCwNZdf4VON85tyXY3nzgQhHZRUQOAcYAv0mzx6pnfGzfWuYcwzDKMR4dnlPaYzQzzWV27okNwzDqS9enHBaRHwJz8Kkofwu8QUT+0zn3+UZ3rr4sUlPuF2G6JoZhDCZdyuF6K38757aJyCV4J9UDfNs5t0ZEfgP8EPgG8Hn87JA/As8AH05qG636q8AuwGIRAVjunJsTrftWfCaebcDFzrntdf1SA1jKYcMwqiNNyuFmZWDIzj1xNegw1ka226dylUGMTt+kXFBsJf4i/bjYu/hR6jZyY+omAPz6vhra1LCdmh43H11LI+jTsd5V8MDT6dvwYPomnucqVxmEliqoBi1n0N50UjactFQjwHGMc+5PIvI+YCH+SeVv8TftHcRp5HmAPP8IfBGfRrSgiVBJ1yLpB0DvwjQ6C3q94br2VmW7x803q+KDyiyXssO2e6qyTSQTfj39GzJI0+SVMsuQTkskzT5uF40S3cekS02XJdXV61X7dXNwnmxSVZX9wnP7xuxn9j9wYPn36mZk1PFK02RbmWWAE+Pmil3idykPcczA8h+ejm+HJ9S6Nik7ds4N5VgntT2bPPeT5zLgGiqlHG6E8rdzbiHe34YMd859SfviKIVwpbY45w5P2N7VwNVD7nhFxrKBeQMpRG2+n2EY5RiLV6uuJuVwEzMwZOSe2DAMo7402Rc3nWo0TXYWkZ2B2cBPnHOv0zCRwEYSphzOA0tb3SHDMNqSn5Hjdvo4gRy3UznlcNOmImbAF1vKYcMwqiNdymHzw4ZhGK0k6+E51QyafB3//HcP4F4ReSu1zS8qi4jMEJFHRGSdiFxWolxE5MtReb+ITEy/FZ1yuLxOgWEY3UxTUg7XQkN9cXP8sKUcNgyjOto05XBG7okNwzDqT1cPmjjnvuycG+WcOzfKtvAkcEa9OiAiPcD1+JnaxwDvEZFjVLVz8OKEY4CLgBvSb8mnHF5FmEbUMAxD81PlK5aUrekQttGT+lULjfTFzfPD/bF9219zjw3DyDr9oHxxaY+RFT8MzfTFhmEY9aWZvrgVVCME++/A55xzm6KPRgD/BHyqTn04AVjnnHs82t4twCy8KGGBWcDN0Q/UchEZISIHRNkiqmQyec4hz8XAV4CJlH84kEbEKo2GSaXthLole8WLtPaI1jQZXWa5lK3bhmhdEv31NpdZhsEaJzG9jbT6E+G+qdS2XiJJaePwko69PrY7J5Sl2Y7+rgnfXR+fjXFzx/o9YvZT+xezzv6OI2Nlhx/7WMx+8y4vFQ212x49JC6i81veFrNXc1zReHDXeOP1yX2Oo/d/0nmS5jo9kzzvbKmmSSka7Iub5IdN08QwjOpoR02T7NwTG4Zh1BfTNIFzgh8HnHP/C5xbxz6MAp4K7PXRZ2nrVGAZOe6gjxw5FgE1SE0bhtEFLK5a0wSaOhWxkb64SX7YNE0Mw6iOajVNIDN+GJrmiw3DMOpPV4fnAD0iskvBEJHd8Gkr64WU+EyLalVTBxG5SERWiMgKeEmVnqliY6ek7qhhGN1ArmpNkyaLXjXSF9fND4P2xVuCEtM0MQyjOqrVNMmQH4aG3RNvKdHEMAyjfjTKFw9F56lcWxE5XkSWi8iqyE+eUKkf1cyh+T6wRES+g3fKfw18r4p21bIeODiwD6IwEzNdHZxzNwE3AYgcoX5AFjIX/8PrY2PvpPzAiZ7mH4bN6NCKNCEBenfvlmCr38SRqqpOIzw6WNaJRXXdEUEczWYVHqHRXyEp5fAgwn0zlDCmCml2E7dT6RRPEyqjj3WtbSu1S7OvEvbFJrUdHerybNx8KrjE1qmTKBZSA7w85omB5S3qPF7FhJj9aybH1/VCcOP7cHKfBqexDi/reqaXDtc1X/mKxcDZZVs2cZS8kb64bn4YtC8+MDhoq2L7didQZ4thGIZnFbCS0BevopzHyIgfhobdEx9YcoDbMAyjntTbFwc6T9Pwvu9+EZnvnAtDFkOdp8l4nafJFdp+DrjSOXeHiJwb2VOS+pL4j1JEBPhvvPrWWfh/8r3OuUXpvnIi9wNjROQQ4GngQuC9qs584JIotnMy8JLFbhqG0RhKPcQrTUH0qtE0wRc3yQ9LgmUYhlFksH8o7TEy5IfB7okNw+hQGuSLa9Z5wk8pKNfWAXtH7d9AmYeAIYmDJs45JyLznHNvw0/NqDvOuW0icgmwCOgBvu2cWyMic6LyG4GF+JjRdfg5hh9Ov6Up9HL9wDTPPKfXpf+GYWSNHL1cGfiK8unJmyV61Whf3Dw/PJ5efjKwb39cl94bhpFFxgMXQOCLx5eslxU/HG2jSb7YMAyjvjTIF5fScJpcRZ1RFdr+A7BIRL6An/h8cqWOVPPNlovI251z91dRtyaccwvxPwLhZzcGyw64eGhbuVdNuV9GUlYMwzC6FR3KtwT/ULHlNNQXN8cP98f27TAo8zfIMIxupx8dntNPG3iMjNwTG4ZhtA0jvfbSADdF4YUwNJ2npLZ/B/yjc+42EflL4FtUuNmvZtDkDOBvReQPwJ+jDjjnXIdp+B1PnjPI8yHgu8DbKK+JkLRbKukohHoUej2V7EAbYoQqStIw0bYq2/WgF2P2Lru+NrD8kt7QMKVxkqRpMhSZkoq8XmYZkjVNKqF1ZFqRGiutFkdS/YQUxK8qTZMKKYif277/wPJTPQfHyh5RKYg3BefNyyo9dh/Hx2yth/L6qr2LxjrVJ61psknZvByuSZUlpSBOoz8zlTx95PkEPsQxeXC1ibH0GfDFlnLYMIzqqDblMJgfNgzDaAdq9MUbnXOTypQNRedpeELbDwIfj5b/B/hmpU5W848xI/e1vyHHPfRyD3OBPEcA72h1pwzDaDuWkGMevcwLfEXpweeCUniTyIAvLqYc7sMnEG35c2PDMNqSMOVwH7CyjMcwP9xu6IdT1bB35SqD2L9yFc2xNWwGGD9qVeo2E0jfhrXpm9SKjm+oqs0p6du4OTVsCLiNv0jf6EcVkluUYkXlKqV5ooY2L1auMohakh7UM1FC9TTIF9es8yQif0xouwE4HVgKnAk8WqkjFQdNnHN/ABCR/YAazsZ24RR6+XoQG1vDlW8YRhdwLr1cEfiK5JTDzRAghKz44nH0Ms80TQzDqMg4tKZJ+ZTD5ocNwzBaSyN88VB0nsq1jVb9UeA6ERmGzwd7UaW+VBw0EZHzgWvxA/7PA2/Fj4WWnyfZltyndAruw2aaGIYxmHSaJs0QIISs+GLTNDEMozrSaJqYHzYMw2g9jfDFQ9F5KtU2+vwXeK2Oqqnmm/UCJwJ3OecmiMgZwHvSbKQ9mEieKeR5P/BfwESKGgh6KmEa/YOhTIHS2wnskapIa5ocruyjiotvOCouDLHvLi+U7cH2EfERwc27qgcnSc9RUl0XaS+iUJ+ikoZJkrhKmmOp61Y6to2a/qbXG/ZL7wt97gZt9W55Ndl+eVNRm+T5ffeLlT2hhHJCTZMX2DdWtooJMXvtH+KaJjwYW3EcpbMy+NC+Uma5nlSvadLkaeEZ8MWmaWIYRnVUq2liftgwDKP1NNkXN52dqqjzunPuBWAnEdnJOXcPKKXHjmAFOZbSx0fIsZQhBLEZhpFpvKZJHyeTYx5wb9mahR+ItK8ayYAvLmqaHIjXLDAMwyhFqGly4MAngzE/bBiG0Xqa7IubTjWDJptEZE/8P4cfiMh1NDhvSmM4iV6KsbFwUis7YxhG23Ku8hXlNU2AZv5AZMAXj4vtW0s3YRhGOcaB8sXlPYb5YcMwjNbT7YMms/CiKv8I3Ak8BpzXyE41hl8xF1hFITb2V63sjGEYbctC5SuWlK1ZEL1K+6qRDPji/ti+7W9tZwzDaGP6Qfni0h7D/LBhGEbrabIvbjplhSZERJznz9FHO4DvlarTyA7Wj0nkeTTSNPk+PiS18PX1bkjSwaj0QCFJu0OvV6VY2zNYTqFhArDnsX8cWD5sl3WxspHENU22skvR2CO+ns0j3xT/YITabtjHirrxafajtpO0K5K0LPQ29fFIc6zTMBR9E/3dk86hOgosqc2+/urwgeVN6sBviCZHF9gY6Jg8FUuBDg9tPya+4lVqH4enZ1x+Z7CmyaD9mmY/h9tNs99mpNQ0aawAYbZ8sWmaGIZRHek0TcwPG4ZhtJJm+OJWkjTT5B4RuVRE3hJ+KCLDReRMEfke8MHGdq+ehJom9wC/aXWHDMNoSxZXrWkCTZmKmCFfbJomhmFUR7WaJmB+2DAMox3o1vCcGcB24L9FZIOIPCQivwcexSuFf9E5990m9LFOaE2TU1rZGcMw2pZc1ZomTRK9ypAvNk0TwzCqo1pNE/PDhmEYrSfrQrBl59A4514FvgZ8TUR2xifBfcU5t6lJfaszXtOkl0Js7K+BM6Ky3VVdvVtq1fjS69lL2So8583BcoXwnJ2O/3PMPm6P1QPLR/BIrOyNbIrZW4Lv26O+2wuj4+ljX1r35pgdS4U8QvVRh+u8Gn7/SmEwSWEYafZ/I6eFpelHUshNmnAcTaX02EG5Ph57KnvQaV50XC+rc/U54imIXwtCvNZxWKzsxRWj4ut9MG7GwnPWqzKdFpk/KTsMzUoTKlfp/AvX9RPlKxYDZ5dsVYjfbCTZ8sWrYvt2J1AJqg3DMDyrgJWEvngVpTyG+WHDMIzW0wxf3Eqq+rfmnHsdeKbBfTEMw2gDpNUdKEvn+2JJsAzDMIoM9g/t4TE63w8bhmEYaakme05GOFVN85zSwr4YhtG+nK98xbTE2tsZlvrVvYyP7dvxre2MYRhtzHh0eE55j2F+2DAMo/Vk2Rd3Tk+HzH1qyv29wOmt7JBhGG1JXvmKJcBZJWsW4jfrjYjMAK4DeoBvOueuUeUSlZ+LT3/5IefcyqS2IvJu4DPA0cAJzrkV0eejgbUwENe33Dk3p+5fCiikHC7s22HYwIlhGKXpR4fn9FPKYzTKDxu1/kXQYcTVsHflKpoRNWxGh75Xyb6DU/tVpIft6Tc0Jn0TgMmTa2j0lspVBvF/0ze5ep9/qmFDcNsDf5W+0bwaNrTpuRoaweD48mp4uYY2tUpENJ+s++KKHlFELgF+4Jz73yb0p4GcTp7HyfNR4Fv4STa7AXfj/xA14iDrHw71o6D3figfolMMHxs3j9t/dcw+nlUDy8fwUKxshNI0eTkQt9idLbGyzbvEtSx+c7jSNHk4WB4ZLxqkmfFqmh/OSimIayVNSmGtq6L7UKk8JM3NRqW64XfQ+1Tr5AT6PCNUkT4+2g7YonR+nhmUcrh48P+4Rv3yrlIre1jZT8RWNGjLcbSmSb1SDm9TywuB84CfAtPIs5I8lwHXUDnlcH19h4j0ANfjp7isB+4XkfnAmRR98Tn426sxwGTgBmByubbOuYfw6jLvBL5eYrOPOeeOr+sXKUk85fB04AG8vGM/cBxdNf3RMIyAHcBqiv7gaGAR1aYcbs6NenbuiQ3DMOpL1w+a4P/K3y8iK4FvA4s6Mw/9r8ixmF4WMxfIswj/MBb8gEnpJ8mGYWSdPHB59AJ4Nzlup5fbI19xOEn+oQGiVycA65xzjwOIyC3ALGAPir54D+DmyBcvF5ERInIAMLpM24ecc2ujz+rd3xQUUw734f8QrST+cMhmnhhGd7Ia7wvmRfaTEPMXK1lDOQ/RRPHBjNwTG4Zh1J8sC8FWfKjnnPsU/mnmt4APAY+KyL+LyGGJDduO01RsbEj5lKKGYWSdWcr+dMqUw3WP3xwFPBXY64FRyhcfD3w+8MXro3Yl21baIHCIiPSJyM9F5NQq6tdIPOXwzEGlhmF0K/r6n0malMPNiaPPzj2xYRhGfWmmL24FVc2EjkbRn41e24A3Aj8Skc81sG915l7m4iMH5g4qW9LszhiG0Tb8RNlXKV9R3j8MISf9SBFZEbwuClZbaiqIg5gvfg3YTuSL8QF9LqltAs8Ab3HOTQD+P+CHIlJDgHk19Mf27YJBpYZhdCv6+l8AyheX9hBD8MM1kY17YsMwjPrSbF/cbKrRNPkY8EG8+sA3gX9xzr0uIjsBjwKfaGwX68W55NmNPFPxk8LXAh8HvkKSZkF6Qs0J/b9D2VoTJBSoUpom+xz/dMw+jrimycn8MiiL31iM5IWYvSkQu9iLzbGyl5VGxpNHHhyzn3340KKhtSpGKHtjqIuhtTj0qVfJTmLnMstpSathkqSvkdR/3UdtJ2nh7KPK9o2buwbL+vzS9q7K3lbs86atI2JFL2yLb2fzE28qGivUeh5U9jplPxtuU/+f1yJZr1SwQ2o99jOA+4FPA1cB/0yeQyJfsQQ4I7F1jQ5/o3NuUpmy9UB44R0EbFC+eB3wPefc9yNf/Co+7H94qbZJHXHObQW2Rsu/FZHHgCMYfGTrwHHkgXykWnB5NBF/Jv4PUmnFAsMwuoGx+JCcgj+YDnyW2QP+wqselaaRN96RVtQK4GngZ2TintgwDKP+dNIgSFqqmWkyEninc266c+5/ovz0OOd2ALmG9q6uFHRLBBhGjjvoYwY5FuAz6RiG0Z3cSY7/oY/jyPE/FMWhhUoi0Q0aVb8fGCMih4jIcOBCYD6BLwa+BLwvyqJzAvCwc+6ZhLZlEZE3RX8KEJFD8VPPH6/UydrYCa9JIMB41lLULDgQWNOYjRqG0QGsIe4P1gKhvyh3y9qEp5sfH+hOZu6JDcMw6kvWZ5pUo2nyaefcH8qUrS31efsztWrNAsMwss4s5Q/Oq7qlw4tepX0lrtO5bcAlFKfE3eqcW4OfMXJOVG0hfmBjHfAN4K8rtEVELhCR9cBJwAIRWRSt6zSgX0QewIf6zHHOvVj1ThgC44hrFpimiWF0L7X6g0b44QIichB+8ss3Iav3xIZhGEOnkb64Hegc9ZW64jPo9FKIk10MnB2Up0l3q3fhbmWWYVD4gA6XCLP7jo4XHdnzSMyepGbOn8x9A8uHPvhsrIzn4+ao/Yr/h/Y8Nh4O8bLKQ/t71ZFnDw/Cc3S+e/19YmEZel9U2DeJaXaTTtu0YT5JITZpwnHShNzoNMEVwrhiITn7J9c9qMwyVE4Rva0oh/HSsyrsZ6P6PuGx1WFa2tap7DeFhg7H0SmGdThO0rWp93m16YlvV/5gHn7yRjVIQ0SsnHML8QMj4Wc3BssOuLjattHnPwZ+XOLz24DbhtjlmliFz55T2Pc7ARNa0RHDMFrOKgb7g+qo2Q+PFJHwZuom59xNqs6X8CE3+ofbMAzDiNGYe+J2IbvfzDAMoypqT8Gb9Zz0jUbv+VYmQzYMo7XU6g+G4IeTtKUQkRzwfKT1NKWWDRiGYXQLWb8nrn4gP1NMU9Pxp7WyM4ZhtJQLlD+Ynap1luM3G8144tPxx7e0N4ZhtJKh+IMG+eFTgPNF5AngFuBMEfl+um9lGIbRPWT5nrhLB02WVJ1S1DCMrPMT5Q9+2srOdBX9xFOKWsphw+he2s0fOOcud84d5JwbjRfVvts591ct7pZhGIbRAro0POc08qwhz6X4lMMnU72OSZKGibZVmW46QtmB5sROh/85VnQEcU2TtylNk0N/HeiYLCKOTjh6YHHxrX/+Y6xo0uTfxuyHOCZmrzqqqDjw4kGj4uvVmhkjguVNOlWu1plMSiWrdS6SjpXeyWnS0Or1Vro8wvKkNMHa1vsihb2rmrCsdUsOTyjTx0d/vTD7tNYw0bokD5dZLlV3o7JjWiNpNUyq1SmBwcekHFPJ8y7yfAr4N+BM4KWqWjqko0Ss2o2xwB3A7cRVbg3D6D5K+YN5VbQzP2wYhtF6su6Lu3Smyb3kWBCkHF7W6g4ZhtEyfkaO2+hjAjluwwtDV4eLRK/SvgyPTjFqKYcNo3up1R80ww8755Y65yylsGEYRhmyfk/cOT2tK1Pp5XMDcbN5zmxxfwzDaB3n0cvlgT/IMSjlVAKdFI/ZbowDLqCoYTAotY9hGF1DKX8wr8q25ocrIaSbeUsN9QvU8tei2pmhAbvWsJka2TRoanhlVnNc6jZj/kZP1a2Sc9M3+eMknUKxMl/nb1O3ueqFT6duA/i8VWlZWsuGas0UXv19YpE0s6WH0qZ1ZNkXd+lME61pcncrO2MYRkv5qfIH+apbFpTCsyp61WjaTcPAMIzWUas/MD9sGIbRehrli0Vkhog8IiLrROSyEuUiIl+OyvtFZGI1bUXk0qhsjYh8rlI/unSmycnkOYc8FwPX4zVNluC1DO4GpkDZg6hH3pNsNXKuR8W1HWhO7Lf/c7Giw3ksZk/YuireNtSy1TqWWtPkYMpy/FEPxuxfv+F3MXt0z+8HlgdpmryZ8vam3VXh/uU7ASTux0T9E40+xZM0MpL0aUoRlmsNkySdEv3dVdsRqnhkmWUYvM9Hl1ku1TZR00SVPaHsdcFyJQ2TV5Ud0zHRx7KSnYYk97YNHz2fA14lz2zy/F/gGvz1v6WqLTiE7Tvs5rtWtIbBdOAB/BPnfuA4unZk3zAyzw5gNcXr/Wi8JFstmibmhw3DMFpLI3yxiPTg/6xPw//juF9E5jvnHgqqnQOMiV6TgRuAyUltReQMYBYwzjm3VUT2q9SXLh00WUaOO+jlDuZSeK58B3BtUGdqKzpmGEZTuAP4VPTyQye9zIv8wXjgpOpW42DbNrtZr5VQw6AP/4dpJfE/SpaG2DCyyWr8tT4vsp8k7g+q1jgyP2wYhtF6GuOLTwDWOeceBxCRW/CDHeGgySzgZuecA5aLyAgROQD/+Lhc278DrnHObQVwzlWMt+rSh3hn0ksxbhYuHlRuGEaWiev5xf1B9cHBzgnbtw1L/TI844jv+5klyg3DyCb6+p5J3B9Ue/2bHzYMw2g9Q/DFI0VkRfC6KFjtKOCpwF4ffUYVdZLaHgGcKiK/FpGfi8jbK32/Lv3VuJu5+B9lr2Fw/aBym2liGFkmrlsS9wcLqXamif+BsCectdKPn1lS2Pc6krAfm2liGFlFa5YswM8wKfiDam9QzQ8bhmG0niH44o3OuUllyqTUpqqsk9R2GPBG4ETg7cCtInJoNFulJF06aHIWeXrIMxUvBjI5+rygcXKqqp+0m3RZQl1dpDVNAiHr/ZUq82glKrHHqh3xtvcVFx9dES9aFzc5KvhncoiK4Np5VdweffrvY/aBPDOwvHK0WvFBCfYmVfas1vVI0hPZS5Ul6VxozRKNVqGuVD9E93GfMsswSLdk10CjReuQ6P2my5M0TUYk1NVl2tZsCpa1Tom2nw2WtYbJJmUP2uevlFkuVXcoquFJx3YKfhL4ZcC/k+dQ8kzH+4MTqt+Ew27Wh4APiFpNPlI1uDyaqD8T/wdqbOu6ZhhGgxmLD8kpXO/Tgc8ye8AfUG32EfPDhmEYracxvng9cTXOgxj8jK1cneEJbdcDt0eDJL8RkR34f1F/LNeRLg3P6QHOwg9AnQX8khx30EeOHHcAy1raO8MwGs0SctxOHyeQYx5+RLPgD6p3+M4J217vSf0yCuyEn0siwHjWUtQ0OJAUmgaGYXQcoabRgRQSfxb9QbW3qOaHDcMwWk+DfPH9wBgROUREhgMXAvNVnfnAB6IsOicCLznnnqnQdh6RHoeIHIEfYNGPgWN06aCJRmucmKaJYWSbc9U1X2s4nrBj+7DUL6M0WuPENE0MI7vU73o3P2wYhtF66u+LnXPbgEvwuQLWArc659aIyBwRmRNVWwg8jg+u+Abw90ltozbfBg4VkQeBW4APJoXmQNeG52juUpoGd+GzE5Wi0i5LEe6hVxWE5+zFy7Gi/YmnIOZx1fbR4uJKVaTDc8KAh0N+rwrVhCcdJhT2Y8+D4jOYNo9+U7xxGNKxmWQ2qrS720Jbp39NSktbKfwmTbiHDsfZV9lBH3XYzOgE+/AKdXV4zohgWYd0pYgOG5T6d5Oyw/HVZ1WZtsO6+tgOugSGcrzShE/p9N9Jx3qBuuYXA2en2JbRCFYR1zjZCZjQyg4ZhtEwVjH4ejcMwzCMEOfcQvzASPjZjcGyY3BWl7Jto89fA/4qTT9s0MQwjC4kcTA53Woslr5uaMWuUgpehmFkg7pd7+aHDcMwWk/GfbEN7ANwlpqqf1YrO2MYRsOZqa75cjPLKuDE/0CkfRklGU98ur5lzjGM7FK36938sGEYRuvJuC+2QROgkIJ4FYWp+ne3sjOGYTScO9U1v6S21Thgm6R/GSXph9hx0SlJDcPIDnW73s0PG4ZhtJ6M+2ILzwHgDPI8TJ5Lga8ApwFLYSAl8dmUz6iRpJmhyirJMwR6FbsrHY/dtK7Hn1Tbl8oXJSZx1X3aGje1tsqIQAhj3z1eiJUlappoPQ2tzZGUtnbz7vEybaeRvUgMy1AX7p6qWGuNjA6WtU7JUco+tri407F/jhUdvP9TMXuEEhvpCb7ga+wSK3tZpWPetHXEwPJLG0fEynhW7XStRbKxzHIpe1OZZWDwPk5KK1wp5XASadzXNrxuyQzgTuBk8pxPnn8CrsVf8zWS6vwzkhgL3AHcDjyNzxPnKCYftVF+w+hsdgCr8aKv2/HX+e14ObVzIEo6XgPmhzucGv6O6PvKatD6bFWy+oX0MsU/2vcvUrdZd+BhqdsAbD8w/f77NSekbvOTxy5M3YYv1Pin+Pu1NPptDW2eqGVDDL5/rYYucFQZ/oo2aALAveRYQG8kDpkHYAHwuai8BxOJNIxOZjFwZfQCOJ8c8+llfnTNH0VNYXmOTP9ANJswBWkfPi9cmFfOwnUMo7NZjR8YmRfZOYrXe80pxs0PG4ZhtJ6M+2J7cAfAVKVvcKkqr1HvwDCMNmGGsv+pPimHCz8QaV9GSXQKUl1mGEZno6/juqQcNj9sGIbRejLui23QBIAlSt/gK6p8cZP7YxhGfblT2dfWT9Pk9RpeRkm0xoEuMwyjs9HXcd00TcwPG4ZhtJaM+2ILzwG8pgnkBzRMpuD1Lf4R+GJkF6g0JPZKmWVg297JTRNWvV0fKr2q/YqLo5+PF+2mqo4Jjf1V4R7l++DXVdRW2VcJXfxhtDrzN+5cXNZn2ghlJ2qaqDIdx7o5oWzQPlWxlaHMx0hV9SBla92SQKeESfGitx75cMw+jtUDy0fySKxsP56L2bvwWszeQlHDZQMHxMoeU51at0sxHnbLrnHtl9c3K00Tvc+fLbNcqu6mYHnQPn5Z2UqPJ3ZdNGuI+QzgAeAT+LC7D5PnqOCaP6O21Tp8YL5RF+YxF1hNnnHASiaSZyY+WHJsa7tmGEYdGAs8CcyE6L7rPPJMoKhcVAPmhw3DMFpPxn2xzTQBvGbJWfg/1GcBy8iRp4+p5MjjRWENw+hc7iHHPPo4mRzzgPuIX/NDSHmW4amIzWcnvHKJAMMG9E0OZAh6B4ZhtA2hbtEowPtewV/3Q7glNT9sGIbRejLsi23QpCTTVFy9aZoYRmczoz4aJpqMx2+2lnH10TswDKNtGKxbVIcr2/ywYRhG68m4L7bwnJIsYi7+B93H1efxuyqcyl94Mq2DsYKQFF2mTwwdShLYm1T8yvNh/A2oGBti4SHTXowX/SmeGZi93xIYx8bLODBuhqEhEA8dGUl8xfscFI8LenHTqKJRKTxnk7I3J5SlCc/R6H6EITk6HEfvm0nxlY8ftapYxIpY2duUPYFi3cN4LFa215/j8Udb9oiH0YQhOKs4Plb2ijo+G4ID+Pqrw2Nlg0Js1ifYlcJzYl3W14BOeq3DdcLwHN22kvcMD+DOCWXbgLsophjepq7pxdQlI1aDlMJFZAZwHd7RfNM5d40ql6j8XHz804eccyuT2orIu4HPAEcDJzjnVgTruxz4CH5i5cecc4vq/63SskodM3+Ex2EpiA2jUwhTDPfj3eVKwut6FTBhaBvJeMYGwzCMjiDjvtgGTapiLXAHxRTEUFN6UsMwmsRdwNWEKYYbQgN+IESkB7geP8VtPXC/iMx3zj0UVDsHP3Q6BpgM3ABMrtD2Qfys+K+r7R0DXIiXGzgQuEtEjnDOtTgyNa4/tAH/Z2te8JmlIDaM9kanGJ6IfjajdMZqIeM36oZhGB1Bxn2xPagryXQ1ffTjqrxOU/sNw2gQ05V9WSeF3J0ArHPOPe6cew24BZil6swCbnae5cAIETkgqa1zbq1z7hEGMwu4xTm31Tn3e2BdtJ4WMz52zGaqUgvXMYz2R1+nM9HhOTb0aRiGYbQ/NmhSksUVUhDXmJ7UMIwmoaNLPlefFMOa2uM3R4rIiuB1UbDWUcBTgb2egmZi5TrVtNXU0qYJ9MeO2YJBpYZhtDv6Ol2ATitehys543H0hmEYHUHGfbGF55TkNPJsD9KRTsZH5l6MH0B5O0VdBq2rkJRyWFXV+huBbsQGNYH1IY6J2U9P+mXMHnV+IGSyb3y1eytNk5g8ikqV+7rS8dDaKtuDLCO7qVSy+/fEU+e+dnhRU2Pzrm+Kr1in99VphTcllCWlINb7uJKWyuhg+djymiUAk/l1zD6Z4jE4lXtjZYeuUqIgocTJBtUHlQl4V9WPXaYWozKe2uXgWFmPyu31MnsVjfVqxUkaJpCccniTsre5wNAaJtp+JcFulLc8A5+nIUwxfMTQUwyXoravsNE5N6lMWan56q7KOtW0rWV7LeC4KCVpQQ3haCbyWUtBbBgdRJhieAGwkstZydrguq4xzbCmg268DcMwMkuGfbHNNCmJTkH8S3LcQR85cizCpys1DKN9aWCK4RCH17JN+0pmPRCOjh3E4KG2cnWqaVvL9lpAmH54PLDWUhAbRocRphj2j4LWEr+u63Ab2hg/bBiGYaQh477YZppUxZn0cu1ADG6eKa3tjmEYFZhOL1cH12yDdIgcUH+51PuBMSJyCPA0XqT1varOfOASEbkFPxXuJefcMyLyxyraauYDPxSR/8T/rxkD/KZu36ZujKOXeQPH9Mct7o1hGJUZB1wAgS9ugBpRY/yw0VT0jNQq2FTDZh6soQ3w+ry9U7f57ykfTt3mp28+L3UbgM0bR6RvtFTPlK+CeembkK+hDcC2WkL31tbQ5sXKVUpSwznbSSMEtZBxX2wzTaribhWDu7SFfTEMozKLGqNhomlA/KZzbhtwCV6YZS1wq3NujYjMEZE5UbWFwON40dZvAH+f1BZARC4QkfXAScACEVkUtVkD3Ao8hM/PfHHrM+eUIq5xYpomhtH+9NMADRNNxuPoDcMwOoKM+2KbaVIVJ5JnOnnmAF8FtgKvAfcC7yA+1T/FLt2k7EBHYv1jh8eKVhwWlz84OKbbCFMvuGtg+a3H/jG+Xj2Iuktx8fVD4kUPvWFMzNbaKluDxsPUcOJevByz99vj+YHl4Ye/Fit7eeReMfv1zbvFO7IpGAFP0jCB5AtuRNzcafSfY/Zh+68bWJ7Eb2NloWYJDNYtGf/go0Vjodruz5X9KOUZo+w94ubwV3cMLG/ZZfdY2XMxgRrY8NIBRWMdcbT9hLJDjZONqkzv89hJpU+wl5VdSeMkiaQnIfpaE2AxMANw5JlFnn/Fa5qclmKbKWhQejXn3ELUWeWcuzFYdniRparaRp//mDITNJxzV+NzNLcxY9nAPG7Hxw5NBx6AmDKCPQUwjNayA59muKhE5EdwC9dtQ9SIMp7m0jAMoyPIuC+2QZOquI8ci+iNnl7nWQLcEJSf3qJ+GYZRZDFwZfSCHNDLT6Jr9gi8lkmdyfgPRHuxZkAboQ//R2wl8dnClrzUMFrLavw1OS+yn4TYdbuSNdT9SjU/bBiG0Xoy7ovtwVxVTKGXYkxunAY9wTYMIyUzYlb8mm2QpglkeipiezEudkxnDio1DKPV6OtwJtoXN+hKNT9sGIbRejLsi23QpCqWqpjckHsHfWIYRiu4M2Z1qqaJUY64psmCQaWGYbQafR0uwDRNDMMwuoKM+2ILz6mK6eQZHmXguA0vDvE3wLfwmorl0CrJLm5ukrj9RLC8Kl72y9Enx+xderbG7I3sO7B85Jjfxcr257myPXyZuLaI1jB5KpaJFF4ItrNNpW3dTWlVhBonPT3xq2L3fbfE7C0jdld2UePk1c3xMraVTxe764i4nsb+b3g+Zh+mhD0msGpgWWuYnLX9rpi993x1POcHy+o/eX9cciam6qElTPY/WH3whrj51BvePLD8CEfEyn7HkTH71VX7FA2tEp9G02SbOlcH6ZaE51RaDZMk9fAa1NwHOBU4H/gn4PPkOYI80/EH54whrDeBjE9FbC+OI08h+0Y/l0cBADPxf8waoJRgGEZKxuJDcgrX5XTgs8weuG69+lCdMT9sGIbRejLui5sy00Q8XxaRdSLSLyITy9T7roj8XkRWRa/jg7Ip0WdrRETLbDaYHrweggDDyHE3fbyXHEuAXzW3K4ZhlOEecsynj9PJkcePCQv+2i0/0DYkOignfef74Z3wWggCjGctRa2EA4E1ze2MYRglWEP8uvQJQIvXbUNuOzvID0MqX3xJVMeJyEhV1kJfbBiGUYIO88VpaVZ4zjn4B+xjgIuIq6hq/sU5d3z0WgUgIiOArwHnO+fGAu9ubHeTOFXF55qmiWG0B9Obp2NSoJCTPu2rNWTID3tlhPB4m6aJYbSellyXneWHoXpffB9+1P8P4Yft5osNwzCATvTFqWhWeM4s4OYoTeZyERkhIgc4556psv17gdudc08COOeer1C/gfycufibAR+fezd+N56G1zc5l+JTbT1HSYUpbFRhJ2H4xIp40Yu7jorZi6ZMj9kb9iiG1RzGY7EyHZ6zG8XQmO3qFHiZPWP2Jt5YtlyH9mxXT/N3YWvZMr3dbT1qJkDQjV12jacr1uy2S/H77E/81BjN72P2cayO2acEITmnbl0WK9tj/o6YPSiJ68+Ki7/eEC9aHzfZP1w+UBVOUvaZcXNZMDD3S+JhWmsfUQ+olgfLD6v16vAc3clt4XCvDsd5QdlhSI4Ox9H2UObqJYXrbMWH3kzDZ87Zoa7NxcDZQ9h25siQH/YaCSsJj7f3xJaC2DCah04xvI34dWnXYEmq8sXOuT4AEdHt28oXG4ZhNBIRmQFch/+D/U3n3DWqXKLyc4EtwIeccyurbPvPwOeBNznnNib1o1m/Z6OAUOVhffRZKa6Opit+UUR2iT47AnijiCwVkd+KyAdKNRSRi0RkhYisgJfq1/tEHgW+gh/o/wpwT5O2axjdzhLgs/hRps/SsgCNzhG9aoofBu2Lt5SrNiT034gN+DSnV0XvqzEMo9EUUgwXrjv1/GDQddowOscPQzpfXIoa7on/PITuGoZhVEmdfbGI9ADX42foHQO8R0SOUdVKzt6r1FZEDsY/eX2ymq/WrEGTUr+bWmkS4HLgKODtwD7Av0afDwPehtcWmw7MFZEjdGPn3E3OuUnOuUmD1DTrxukqBOAiVd6EkADDMPB+LuRf1LWpyxtAZymFN8UPg/bFu5eqMmTGg6UgNowWUynF8PhmdKKz/DBU74vLUcM98R619dQwDKNaGuOLTwDWOeced869BtyCn60XMjB7zzm3HBghIgdU0faLwCeo0v82bNBERC4uCAniHz6EOUIOYvADCZxzz0RfeCvwHfyXBT8Kf6dz7s/R1Jl7adJv8WCWqfR531DlDUptahiGYrGyr21OmuGQNr9Zz64f9qEAloLYMFpLpRTDTbkO29wPQ22+OIG28sWGYRjAUHzxyMKsuOgVzkioZmZeuTpl24rI+cDTzrkHqv16DdM0cc5dj58Sg4jMBC4RkVuAycBLpeLoCzGdUWzSbIpJU38CfFVEhgHDo3V8sVF9T+Y88uwZpR9eAkzEjz3NAW7EzzQpp8OgUrPqVLpPBMt7Jvdi8+Y3xexfHVsUwnjkqHga2v174pomYSrg4cT1QrT2yFaGq/LqT5lwXXo9r1VY7/YgrXDPsLhK0O498en+I9g0sHxw7NqAw5W+yzE8FLOP4JGB5T1WKw2TuBzKoFsa92pxeZ94Efsp+5BDAuN8VfjhuPmTA+M6HD/lvIHlpc+p1Ll3qXWtCJa1pskTyt6mJavD80RrmiSlEdYphfXdaKNSDJ+Cf9B2KT407iPkOSq4NhuUZjikoBTepmTXD8M85gKrLQWxYbSQlqQY1rS5H4bafHECbeWLDcMwgKH44o1+RlxJqpmZV65Oyc9FZHfgk6QUPmyWEOxCvDjLOnyA+8DfRBFZCPyNc24D8AMReRP+S67Cj0TgnFsrInfif4F34IVcHqQlFNIPE70vJMcielnEXCDPREx80jCawb3kWEAvC6Jrbyzxa7MJFJTCO4MM+WEopiAGn4J43kCq0z68wo09ejWMxhKmGO4jTDEcvjeYzvLDUKUvFpGP4aeOvxnoF5GFzrm/aT9frKllGs/LlasMQj/MqYIVe9ewnRpZkV7RZ/OwN1WuVIpna2izqoY269JEkRVYWUMbGJy9oBr0A79q0A/+qqXNR2pbQWN88Xoqz8wrV2d4mc8PAw4BHoiEtg8CVorICc65sldTUwZNIoXwi8uUnRssn1mqTlT2eby6bZsxhV6+NBC/m2+GjoJhGMBUevlccO21SE+otbHxVZNtP+y1FS6gqKXw45b2xjC6g1LX3bxWdKRD/DCk8sVfBr5cpl7b+mLDMLqY+vvi+4ExInII8DRwIT6DWMh8SszeE5E/lmrrnFtDEBQgIk8Ak9ole06GWap0FLTOgmEYjWFJ8zVMNB0QS98taI0T0zQxjMbTFted+WHDMIzW0wBf7JzbBlwCLMJPZrzVObdGROaIyJyo2kLgcfz0pG8Af5/Uttav16zwnAwzhTyPkOdifLjqqcDP8Jk75uEjfAu7WU8l3Cturg80TvSR0SfVJmUHk4leXBfXx3nxIKWXM6I4vW6nPeP6IFo/pGdYfMPDgvLhu8b1UHp64m23by/qkoQaJQBbX1UaJ6/uQjl2V33cfY+4PZytxTKV3nQvNeVzdzUNb5dQ00Xvc52AaUzclKD+mFfjZRyo7BOD5XfHi/5nv1zM/n/8n5g9/4WiCMqOeUoB/xdqO+EEXT2zcZtO/fqcssNpjXqqrJ6++HqZ5VIMRbckXPd24D58iuG7gZPJc05w7Z02hO3USAfE0ncLY4E7gNvxjxMOxh+egqqCPSEwjPqwA59qeBzeKz+Nv+424PM6zmt2h8wPG4ZhtJ4G+WLn3EL8wEj42Y3BctLsvUFtS9QZXU0/bNBkyCwjxx30ckekq9AD5IHPBnV0MkzDMNJzL/Al4NrIfkhde8fRNC2TAp0XS59ZtLbC/OhVwPRNDKM+rMYPjMyL7BxxLaGmY37YMAyj9WTcF9vDtyFzJr0U43nhH1X5uRiGUQ+mKPtide21QNPEpoW3DeNAnQ/xMsMw6oO+nsLrriXXmvlhwzCM1pNxX2wzTYbMz5mLv1nwugpfUOVX4sXPF+GfxYS7XIdH7F9cfEKlI9Yn1WZlh9I1T6iyNyt7RFHRe8ee8XCPHSrV8eu7qrZhuS7T9rYyy6VsTbCuzXvG90XPQfFhzL12KYaSbCFe92UVAvUC+8bspwJR5eHHx8ON3vQGtZOPUn18KVjWKaJVKM8DBxc/WKJmQyxQA2t3/2F6vHE+CG9Zqraj9fLDkJxB4ThPKzsprXCatMGaobiVMJRnO3APfrBkaYk+Xa+uvSU0faYJdJTDzzJXMZuVzAvOhyIr8WdlmATVnhgYRnWE4Tj9DHZ5oR/OM7upfRvA/LBhGEbrybAvtkGTIXMGeQqZO5YA26KQgcINhC/17Aac15puGkbHUQjH+VJkTyNHeHN+NHmOC669M5rfRYulbyOOi7ztOOCn5OgbOFc24AdO5gW1LVzHMKpDh+NMBOWLJ5InR3FIssk0yA+LyMHAzfhHTzuAm5xz19V/S4ZhGBkg4/fE9rBtyPTgn25L9D697BRxf5thGEZ1TFH236trazrxa68Ho5vZCT8UIsB5sXNFq0pZuI5hVI++XmaiQ+Fy+OtuPBm7rdwG/JNz7mi8nPvFInJMi/tkGIZhtIBM/bq1BzoNakh+0CeGYZRjqbJvbH2KYU1B9Crty2gw/bFzZcGgUsMwqkVfLwtA+eIWX1EN8sPOuWeccyuj5ZfxKStHJbcyDMPoUjJ+T2zhOXUnDNcppIX+OPAV4O3Arfgn5KU0TkL2j5vrlcaJ1jTZFCw/q8pGKntEsKy1ONLYuqxSmuSksiTtlECDBeClV5VIy+HFxZ5dkq++rcRTHW8MNE4e4chY2b6HbIzZex2id3oRrZXyBKNj9gomDSz/kpNjZX+4T4ml3KVWviJY1homT+iehLokWjNH2zqtcFIa4aS0wbsnlJVaVxLb8WE5U/AnyTR8uvUbgQ+TZ3xrw3E0BdEro80IQ3X6uTwKLJiJ/8N3NPAApnFiGKXQGiZHA09SvH6mA59l9sD11ZKQnJDa/fBIEQl/YW9yzt1UqqKIjAYmAL+uaUuGYRhZJ+P3xDZoUncK4ToAw0qkI54PXB2Vm8aJYcSJ65j4uPnFAxomxWxULRB8LUXGfyA6l0KoDsB41jIvlo54EaZxYhjl0BomTxJP570WCK+vllO7H97onJtUqZKI7AncBvyDc+5PleobhmF0JRm/J7aHaw1lqor7/SdVbhonhhFnSsyKXz9TaDsKoldpX0ZT0emITePEMMpTScOk7a6XBvphEdkZP2DyA+fc7XXuuWEYRnbI+D2xzTRpKEtUStRrVXkem2liGCFLY1b8+lkKKj1zW9BB8ZjdSj9+ZkmYTUeXt8HzcsNoC0ppmPRRvH7a8saxAX5YRAT4FrDWOfef9d9Cu1Prv5lXamjzQg1tHk3fZOOYGrYD3Ll3+jY6hL0aan1K/2otjX5fQ5s/1NBmfQ1tAF6soY0OO6+GDvrX3glk+J64LX/7soNORzwZf3FeDFyPT9x3C163YTEwi+Ih0Z5zn7i5SduB7scI1VTbe1ZZVsnWOiRp0GdeoqaJKotLjfDSpqLGyUsHxbVFnntrXBvmMQ6L2fsGP9S7syVWtpuyhylPsJVdBpa1pslTHByz168JfqiXE2eFslcpe12wrL77YJ2S8MbjeVWmZxUn3djoA6Q1TYYllFVaV8hW/GDImcDd+AQF04E5wNeilMLTaRsNE02DpiKKyAzgOny83zedc9eoconKzwW2AB8qCBaWaysi+wD/DxiNV8P5S+fc/0ax+muBR6LVL3fOzan/t2od85gLrC6pcZLHazg4TN/E6F5CHZPtePGOHG2qYaJp3JTwU4D3A6tFZFX02f91zi1syNYMwzA6mYyH59igSUMJ9U3OAhaV0DjJA5+N6uzK4InjhpFlluJnYBVmYU0nxyJ6WRRdIxMophRuQxrwAyEiPfhR1Wn4RzT3i8h859xDQbVzgDHRazJwAzC5QtvLgCXOuWtE5LLI/tdofY85546v7zdpJ5I1TuZHLwZqGEZ3oXVMcrSxhommQTfqzrlf4H+ADMMwjEpkfNDEHqg1lTOVRsM/qvI2DD0wjIZyprLnqGtkapP7k5LGxG+eAKxzzj3unHsNPx1tlqozC7jZeZYDI0TkgAptZwHfi5a/B8yu4RtnAq1xossMo9vQ531ba5hoMh5HbxiG0RFk3BfboElTuZu5+OgLr9HwRVVuMz6NbuNuZd+orpElTe5PSmrPST9SRFYEr4uCtY4Cngrs9dFnVFEnqe3+zrlnAKL3/YJ6h4hIn4j8XEROrfr7dyj9oM6zeJlhdBv6vA+vj7a/Jmr3w4ZhGEa9yLgvtvCcpnIWeXqUxsl2ihonb8eLtE8Dfhq9Fw6R1p/Q+hSBSNUmJVi1SWlOhEd9hFpNJU2TUHskzdmj66bRNNF9GKnsJ8Ky+Hf948i3xO0RcTu27kp91GwOlrXWyBPKDnVJHk4oK9V2W6itojVMtFBWaOtzRtt6eHfnMsuQrHGSRv9kG/Azijo+p+F3yKXAV4CLyDM+uEbaUMdEU/9Ul6Wmg7sq61TTVvMM8Bbn3Asi8jZgnoiMzXJqzbjGyUomkmcmXr/haOABiKk32NMFI2uEGib9+PP+SYo6P3nOi8Ij21DDpBQZnhJuGIbRMWTYF9ugSVNJq3ECXrrAMLLCXfjzu3COzyTHAnpZEF0DY4lfI21OY+I310NMSfggBid8KVdneELb50TkAOfcM1Eoz/MAzrmteEVenHO/FZHHgCMYLFGcIUKNk2ExfZNF+Ew784LabajiYBhDQmuYPAmx66CPHvwYbAec/RmPozcMw+gIMu6L7QFaS6mkcTKjyf0xjEYzXdmXdpaGiaYx8Zv3A2NE5BARGQ5cSFynlMj+gHhOBF6KQm6S2s4HPhgtfxD4CYCIvCkSkEVEDsWLyz6ebkd0MuNi56CW4m57PQfDqAF9Xs9E6/x00Jmf8Th6wzCMjiDjvthmmrSUnzMXf4Pi4+qvUeWfAT6G1304Cz9TpYCeOR+GZeymyuLpcNm2V3F54+7xMh1mosNmag3JSRues2tCmQ7XGVFmuVTdpDTJlb6bHj19NVjepMqeVfb6hLLNOnpCpwoOQ3L0cdc56cNQHh2Oo79A0hfWITbaDs+xanbcYvwg4GdV2XXqGlhCR8wwaSDOuW0icgl+0kMP8G3n3BoRmROV34gXQDoXH9y1BfhwUtto1dcAt4rIR/APlt8dfX4acJWIbMPHC85xzum4rwzTHzsHfUhC30DpSvwZbuE6Riejw3H0r0EvE+ijL/DF/XTELBPDMAzDaAI2aNJSzohihwv6DVvIsTi4eV+E/+8D8dAew+gkFgNXRi+fyrJ4jh9NnuM6S8MkpCB6Ve/VOrcQpQwdDZYUlh1eDKmqttHnL1BiKo9z7ja8mFKXclzkhwt/J3eQC/48bsDCdYzOR4fjTET74oPJ89bgOugAHZMCDfLDhmEYRgoy7ovtgVlLKQyESPR+Vtk0mB0XtmAYA8TDzOLn+HTi10APHUUhfjPty2gjCvomBf2G4y1cx8gclcNxjid+HXTQ7aH5YcMwjNaTcV/cQb+K3cDSsmkw2z71qmGU5c6Y1VEphashwz8Q3Ul/7BxdMKjUMDoPfd4uQPviDj+zzQ8bhmG0ngz7YgvPaSumk2d4FKpwG16u4G+Ab+ETAs4DTgWWAacTfyofak6olMODtC32LrMMg/RQXtX6KGlIOr1KZUatcjVp0hOnTSOchL6wQ3uzKtuk7FD/hBdUoU4jrHVKQh2TNGmEK3mipNTAlXZcaOv1bAPuwE/+zuNDblYDlwH/Tp5DyTOdjgzH0RREr4wMEQ/XuTwKaLCUxEYnkZRSeAF+jt9nmd2Z4Tga88OGYRitJ+O+2AZN2opQt2QYOe6ml7ujeGPwfzK/FtQ9vek9NIzK3AF8KnoBvJMct9PL7dG5/AmK4TgdTsbjN7uTMB3xeNYyz1ISGx1HpZTCa4HwPO9ozA9XQS3/Zmr9i1DLvyb9sKganq6hjX7QVC36AWMVbK5l/9Xav1r2n36AVw21aMTX0jeobV9k+B97J5BxX2wPyNqWU1W88UdU+WlN7o9hVEtO2Zd1dlrhJDIev2n4J/WmcWJ0GpU0TDJ13pofNgzDaD0Z98U2aNK2LFPxxt9S5fc2uT+GUS15ZV+TPR2TAhn/gTB84IJpnBidRiUNk0ydt+aHDcMwWk/GfbGF57Qt55FnzyAV6yjgNeB9wA+Aw/G3QScCyxmscVIteiqb1jDRehVDWXcagu1uU33anGSn0EqpK/q7/knZLyaUJWmYQHyKYpp9qi/vJA0TgN0T6layFwLnAT8FpuFVHz4NXAX8FXkO79y0wklkPH7TgHnMBVaX1DjJ47UjHKZvYrSeUMdkOzABP+8vcxomGvPDhmEYradBvlhEZgDX4f/oftM5d40ql6j8XGAL8CHn3MqktiLyefwfl9eAx4APO+c2JfXD7u/aFp2OeBU5ltHHHHIsA34EfAf42+j9Vy3rqdHtLAQuB46N3r9Ajv+hj+PI8T/AfXR0WuFKbK/hZXQQ8ZTEaylqQ4wC5uOHBufh/7AaRqso6JhchR++HoU/Tw8k1DDpwJTC1WB+2DAMo/XU2ReLSA9wPXAOcAzwHhE5RlU7BxgTvS4Cbqii7WLgWOfcOOB3+D8wiWTsVzPLnKh0Id6nyk9pcn8Mo8B5yv50djVMSuFqeBkdi9Y40WWG0Sr0+ZdZDZNSmB82DMNoPfX3xScA65xzjzvnXgNuAWapOrOAm51nOTBCRA5Iauuc+5lzrhActBw4qFJHbNCkY1iudCG+r8q/jT/zfoE9QjEayzb8c0wXvd+myj+TXQ0To+vRGichK/GBaS5639HUnhndxg7i59tKVZ5ZDRPDMAwjS4wUkRXB66KgbBTwVGCvjz6jijrVtAX4a3zqz0RM06Rj+EvyvCnQhXiSHP4Jkk/juhRYGtXtIZ5dp5KWRchQFHl0INu2hLI0aJ2VJLuS9sZQ0N8htPV+06nS/lRlWanypGOSpFtSSdNE27uVWdbrWgRcQTGl8LvUufhW8nwimxomRtcT1zj5KTn6Bs79DVg6YqN56LTCE0H54onkyZE5DRPDMAwjS2x0zk0qU1ZKrFLPTylXp2JbEfkk/o/WDyp10maadAxa40SH64Sc2syOGV3Hucr+jDoXp5NpDROjywk1Ts6zdMRGy6iUVtgPoWRUw8QwDMPoBtYDBwf2QfhnVNXUSWwrIh/E/1C+zzlXMVDIfkU7lhVlp4jDsmZ3xugqFir7KgvHMbqUfktHbLSMSmmF7Qw0DMMwOpz7gTEicoiIDAcuxGvwh8wHPiCeE4GXnHPPJLWNsur8K3C+c25LNR2x8JyO5X3kOTAKgViEPy8K6YjfDtyLn3GyDDg7ej8TuBsvIlx4+p8mFESTFI6j150U2lKKMHSkUnhOUkhKJeqVUrlSeE5o62tTt00TjpO0LyqFZVUqX4SfVbIQmIEXms7hI+nfBXyGYkrhQ7o0HMdyXXY3x5GHII3r0Uzks8zE/4E9Gq81MQ7/R7bwzN/SExvVEqYRXkX8HDoaeBIGzreVXM5K1mYzrXAi5ocNwzBaT/19sXNum4hcgv9T0gN82zm3RkTmROU34v+onAusw//J+nBS22jVXwV2ARb7jMUsd87NSeqLDZp0LIVwHYBh5FhGL8uiOObh5FhCL1+L7N/j9W2ujeoPxw+kGEY5CrolV0R2Pzluo5dPDcxs6uW26Pw6hOK5eBbdhWNoOkBGZ1MI1SF6f2AgHXEf/io6ELgAr3WSB34StDa9E6MSWrckR/F8KpxfhfNtJWuJn4/dgvlhwzCM1tMYX+ycW4ia5h4NlhSWHXBxtW2jzw9P2w970JUJpqo45o8oW59H05rWM6NT0boln4qdU12VUjiRwqh62peRTcYN0jix9MTGUEhKIzz4/OrWs8r8sGEYRuvJti+2mSaZYAlzKSrmw7eUfb2qvxibaWIkowdl/02dU+HyErpvhkkBe8JphPTHrpMN+BkA4XVTrNldcwGM2tCqJMnnV7eeVeaHG0Ot+1SHJzeKWv5s6WyF1VLPTIxJ1PoHspZ93qw2tZ5HnfNn2iiQbV9sgyaZ4Iworr6gKXEaed4W2BPxkdFzgBuBE/HhOlMpShdOi+qeTfXZTrRDS9Lx0GW6bZK+RiXtlLBu2h+2oVwCSY4hrYZLSJL2SJKGSanypPUKfgBtBn6QZCf85O+F+EGQfnxa4X8DPkCew6JzajEA+YFzpps0TDQWS2+EaI2TsaxkTWSvZCL5knonviasCWzTPOkOQs2SUudBqFviz63zyDOBwedXN2mYaMwPG4ZhtJ5s+2IbNMkEob5JqfeF5FhEL4siDYph5FhAL5+LbIAvBOuyWSjdwWLgyujlh0t6uTI6JwoaJgXdksMonlPh+dGtM0wKZPsHwkiL1jgJ34cl6p3cEb3PC9bWjXMGug2tWfIk8fNC65b00UNRDpYS792I+WHDMIzWk21fbA+yuoIpKu750rJx9qZ30k3MiFnxc+JTpltSNdtqeBndR7LeycxBtY1uQB/nUueF6ZZUg/lhwzCM1pNdX2yDJl3BUubi0xX6uOevKDtkcfO6ZbSYO2NW/Jz4N2UvaWK/Oolsi14Z9aQ/dk0tgEF2vLbRDejjXOq8iPtiOzMGY37YMAyj9WTbF1t4TlcwnTzDlebJ2MheCDwMXIoXjA31TnxduDewz6CoeZKkYQJxwa20I4nhqanbJumFVNqOPuUbdbGm+b66T9rePaEsScMk1Cy5E3/s7lH2auAy4N/Jcyh5plM8Rw4Lzplu1i1JItuiV0Y9SdI76efyKECjlObJKooBGaZ30nmEuiWriB/LULNkATAd+Cyzy54n3atbkoT5YcMwjNaTbV9sgyZdQZLmyTCld7KT0jtZg7+V+1ywvm7XsegU4pol8AA55gW6JavJcTu93B7Zn8Dfzpc7V4zBZDt+06gnSXon41nLvETNkzzwk2Bt3axg0Wlo3ZIc5TVL1gJJ54lRCvPDhmEYrSfbvtgeVnU9Z1bQO7lU1Tdti85hhrI/oY7tZaZbYhhtwjgqaVnE6xqdgz5eScfZjq1hGIZhtB82aNL1/LyC3skXVf0vAFvwYT0v4Z+TbYneN+NDdAqvUOSnnvFrtcTLFV61CBTVIlQ0TL12Dl67V3jtrV67JbwEuCvaxs/wx2FY9Fle9alXHdsrTbdkyBSmImZT9MpoHlcxO3Y99jKxrPbUSnzojovetyl7R3O6bETsIPl4rFT1k47zVcxuRpczhvlhwzCM1pNtX2zhOV3PGVGcfSm9kyXA1ih8p3DjvpheFkfhHIU6X4vW9VHg5GZ/gS6nVNrgf4uOz/mRTWQfTZ7jEo616ZakpzFTEUVkBnAdPrbum865a1S5ROXn4kctP+ScW5nUVkT2Af4fMBp4AvhL59z/RmWXAx8BtgMfc84tqvuXMiowWPMkz1si+6fk6Bu4ljdg6YrbiUppgzeA8sUTyZNj8HE2zZLayPaUcMMwjM4g277YZpp0PQW9k4KWxXBlnx2bOhyfLv4Rta6TmtBfI05S2mAdfjOd5GPdg5GW+o+qi0gPXpX5HOAY4D0icoyqdg4wJnpdBNxQRdvLgCXOuTH4UbLLojbHABcCY/En1Nei9RhNpaB5UpAJHRbY51m64jYmfdrgHKWP83jstqwWsv100zAMozPIti+2X2ejAnfHpg7Hp4t/S9X9VRP7ZXiS0gZ/zsJvGk5D0qudAKxzzj3unHsNuAWYperMAm52nuXACBE5oELbWcD3ouXvwUAcwCzgFufcVufc74F10XqMtsHSFbczlja41WQ7zaVhGEZnkG1fbOE5RgXOIk9PFMKxGIA80/B/wA/HR3N/CPgucCw+PfGJwHL8g+4HgEnAb6L1nQCsAE4hPrNh58Z+jQGSLs5KfdCjoWkun6TUwHo9YarghXgnNJPSaYNPxf/3/QTwH1Ga4DBt8BEWftNQak6vNlJEVgT2Tc65m6LlUcBTQdl6YLJqX6rOqApt93fOPQPgnHtGRPYL1rW8xLqMtqH6dMV5vFd2AzVhDQQt47alLy5NmCa40n7cDkzAzx+xtMGtINtpLg3DMDqDbPtiGzQxKhCmKz47+Pws4DZy3EMv90Rx2tvIsYxevhPZJ5Pjl/Ty/cgG+GHUfmcsnKccSTolOm3w7MieZ2mDW0LN8ZsbnXOTypRJmQ1VU6eatrVsz2gp6dIV7yCud2L6J+mppFOi9+soLG1w68h2HL1hGEZnkG1fbIMmxhA4hV6+PhCnned99LIssN9NL78M7JATm9vVjmIGhQETiMfC5/kEvcxLsC1tcPOp+6j6euDgwD4IryVZTZ3hCW2fE5EDolkmBwDPp9ie0caMw/95P55ieuLC8u34P/OhvVK1NQYzjvjA0kwG78fQJlj+sWprNIPsPt1sHc388/NKDW1qOea1bKeZ1Hoe13KsatlWdv8QG/Uiu77YZuUaQ+A+Faf9A2X/T9mUmfFoACNOGp0S0y1pLQ2J37wfGCMih4jIcLxI63xVZz7wAfGcCLwUhd4ktZ0PfDBa/iDwk+DzC0VkFxE5BC8uW4inMzqAfuJ+wvRPhk5anZJw2fZps8l2HL1hGEZnkG1fbDNNjCEwmzxvUClsT1L2vZF9Mz7L6Xvw2pTH44VjC/on78D/TzsFWIYPCzo9Wi7UOTWyT6cxmV4qXbhpdFd2wu+DacCiyD4nWp5GXJekoBFzLl7D5Az85PDLgH8nz6EJOiWmW9Ja6j8V0Tm3TUQuwZ8sPcC3nXNrRGROVH4j/kQ5Fy/augX4cFLbaNXXALeKyEfw0QbvjtqsEZFbgYfwjwguds5tr+uXMhqKD85bHWlmrAKEPOOppH+yADgarzxVbEnQsrP1UNLokqwi/t2Pxl8khf2UrFPiWxf3uWmWNJdsTwk3DMPoDLLti23QxBgCod5Jpfdh5LiP3mh2Sh5R+idPRPooXx+YldLLjVHZI/gBga8F2z29UV+qTiwBPhu9Crokn42+zxqlS7KaHLfTyxWR/c7Ivr1KnRLTLWkdjRG9cs4txA+MhJ/dGCw74OJq20afvwCl47ecc1cDVw+hy0ZLCTVPJgSfV9Y/WURcmwOyo4eSVpeEYLmwX6rTKSm1z43mkW3xQcMwjM4g2764nR8SGZnihJg2B7xP2R+K2fGyj6h1ndbgvtaDaTEr/n0+oezLKtimU9K+ZHsqopE9xhH3RzOVnVQ2s8S62hndv1Lfp9rv3u7ftbsxP2wYhtF6su2LbdDEaBK/qaB/8t2y8eHwDbWuu4Gf4y/OnwOvBfaS6OXwITCvRe+l7DR1C3ah/mLgZ9HyXVHZXYF9R6zHyTok11SwTaekfSmMqqd9GUZr0PonSdocuiyv1vVT/BXwAP6sfqBKuy9aZ73ravunqr/5IXx30ylpZ8wPG4ZhtJ5s+2ILzzGaxIXk2b+C/snbI3sxAPkBrY+Xo9Cdwo37o+S4i16+Etm/I8eiARugly9HZWujsi8FoTGLB+x43XjZ4LYF+9qg7ReisofIkac3GhDJk4tCcgp9Poc8/0B5XZLDE2zTKTEMoz7E9U8qaXPE9VDOZx6jKPo1SA7lqTYUpp51S6UCLvR3J2B+1bok8e9uOiWGYRiG0b2ID43PHiJHOLi+1d0w6sJL9PFujsff1k7gVvr4y8D+MX1cMGADZct023jd5PUmb2cJfUwN7J/Tx+mBvQivS2J0Pmf/1jk3CUDkrc4L9qbl7wfWkXVEDnRwUau7YdSBT3PlQGrjVdFnheUwBW81dpq29drOj4GruCLNVzbalptwboOA+eFqaK4fTiOaX6CWZ7jN2k4zsZTDRqdxZdfcE7e79zAM4F7m8v+z9+bhklXV3f9n0Y0gILaAjA00AjIK3Q0icYg4BZQrtFETzKAmvjHJq9HkzaAkIYb0a2LeJGZ4E81LHMCfRCSoLV5AHCIxRAGbvpdumgYh0kLTDDbaSIuNdPf6/XFO3bvPqqpTVffWeOr7eZ56au+zz7DrDN9atWuvtdJ/N/8l1P851Mva4rad7LfsOH8b2v4m1L+KArVWkWoHvRIiZS3Z7I1GGriZzJWm3Xon23brODJ4qop0WAghBk+1tVgzTcQIsJMspsis+wp8vUn9y/k2r+ryuvPd9mX0Jk2y6D/pTJPDHX5nDvv43ZEYVe8GmmlSJdIkvtN0lqC4LMFvt9Ztte2wJ0kW7ZPONOmdDpvZOcDfk32Bf8TdPzCHAw0czTSZz3H6iWaaiFHj4rGxifsyaGJmxwMfB5YDf+Tuf91kvXcCvw0cDTzb3bfky38ReE++2jbgN939tvJjatBECNEL0kGTxQ7vmsM+3tP3L4hB6HC2nQZNhBDdJh006Y0Om9kC4Ntk/45sAr4FvMnd75hLj5P9GtlAzGuAJ4C3uvuaBut9FDidbOTv2/l62+ZmE2vQZO7H6ScaNBGjxsUjaRPPhX6px/fJzuKKFuv9F1mA+xvC8nuBl7r7D8zs1cAlwAu63EchhOiQWnq1kUA6LISoID3T4TOAe9z9OwBmdgVwPjCvQRPg1cCx+esFwIdprKW/4+4/zI/9QeCdwAeQFgshhpKRsok7pi+DJu7+CPCImZ3bYr0pgGwQvrD8G0n1JmBxt/sohBCdMzr+m9JhIUQ16ZkOHwbcn9Q30Z3BifOBT3g21fsmM1tkZoe4+4PpSsmAiQFPJ/ug0mIhxJAyOjbxXBj2eWqNeBtZVsE6zOztzM4/fBJ+5va+9Wp0OADYMuhODBk6J43ReWnMcbPFB6+HPz1gDvsY9fPaVIehkRZfLC0uomerMTovjdF5qacbOrynma1O6pe4+yVJvVHKu274tDcajDkMeDCuaGYfJ3PjuQP43Qb76sAmlg43QM9WPTonjdF5aczY2MQjNWhiZi8j+4J4caP2/Mvuknzd1aPgH9VvdF7q0TlpjM5LY1Ij293PGWRfBkErHQZpcSt0Thqj89IYnZd6+qTDm4DDk/pisgRN86XtwRh3/5U8tsr/BX6eLC5VthPZxPNG56UenZPG6Lw0Zpxs4p6FkTezd5jZdP46tAv7OwX4CHC+uz86/x4KIUS1kQ4LIcSc+RZwrJkdZWZPAy4Arp7LjlItJht4aXswxt13Ap8GXp/sT1oshBB9pGeDJu7+T+6+NH/Na2TezI4APgv8srt/uzs9FEKIaiMdFkKIueHuO8iCr14PbACudPf1c9zXjBYDq4A3W8aZwGMxnknedkytDLwWuDOvS4uFEKLP9GzQJMXMDjazTcD/Av7YzDaZ2b5527W1f0DN7F35eouBtWb2kXwXfwLsD3woH6lf3eAwkUtarzKW6LzUo3PSGJ2XxozkeRmQDsOInq8eo3PSGJ2Xxui81NOXc+Lu17r7c939aHd/f5d2ey3wHeAe4F+A/1lrSLTYgMvMbB2wDjgE+LN8NdnE3UPnpR6dk8bovDRmbM6LZcG7hRBCCCGEEEIIIURKX2aaCCGEEEIIIYQQQowaGjQRQgghhBBCCCGEaMBIDpqY2fFm9k0ze9LMfq9kvXea2T1m5mZ2QLL8F81sbf76hpmd2p+e9448aNg/5J93rZktb7LepWZ2b5JRY2nSdla+bL2Z/UffOt8nzOwcM7srP0fvbdDe1jmsAm2ci7PM7LHkPvmTpG2RmV1lZnea2QYz+6n+9r43mNnHzOwRM7u9SXtD3TGzw83sa/m5WG9m7+5frweHdLgx0uLWSIszpMP1SIc7pwPN+aiZ3Zavc5WZ7ZMvr5wWd3BOGn4/5W3S4THQYWjrXDS1dyqsxa3Oyfn5fTFtZqvN7MX58upqsbuP3As4EHg+8H7g90rWWwYsATYCByTLXwg8Ky+/Grh50J+pC+fkNcB1ZMHDzmz2mYBLgTc0WL4IuAM4onaOB/2Zunx+FgD/DTwHeBpwG3DiXM7hqL/aPBdnAZNNtr8M+B95+WnAokF/pi6dl58GlgO3N2lvqDtkAfqW5+VnAN+O57OKL+lw088rLS4/P9Li9s+DdLi+XTpcf07a1Zx9k/IHgffm5cppcQfnpNn3k3R4DHS4g3PR1N6poha3eU72YTY26inAnXm5slo8kjNN3P0Rd/8W8FSL9abcfWOD5d9w9x/k1ZvIskSMOucDn/CMm4BFZnZIB9v/AvBZd78PsnPci04OkDOAe9z9O+7+E+AKsnOWMt9zOCq0cy4aYlm2lZ8GPgrg7j9x96296mg/cfevA98vaW+oO+7+oLuvycuPk6WmPKyXfR0GpMNNkRaXIy3OkA43QDo8J9p6Xtz9hzCTwvjpgOfLq6jF7Z6Tht9PSIdhPHQY2jgXzXSnwlrczjnZ5u61bDJ7M6snldXikRw06TJvIxtJHXUOA+5P6ptofpO+P59S9bdmtke+7LnAs8zsBjO71cze3MvODoB2zk8n53CUafdz/lQ+lfc6MzspX/Yc4HvAx81sysw+YmZ797i/I4OZLSH75+rmAXdl1KiKDoO0uBXS4gzpcI8YQx1u+3kxs48DDwHHA/+3wSpV0eL5aoh0eDx0GOb3OauqxW2dEzN7nZndCVwD/GqD9iVUSIvHetDEzF5G9gXxnkH3pQtYg2WN8klfSPZl+XxgP2Y/+0LgNOBc4GzgIjN7bg/6OSjaOT/tnsNRp53PuQY40t1PJTOsVuXLF5JNnf6wuy8DfgTU+TqOI7l/+GeA3679oydaUzEdBmlxK6TFGdLhHjCmOtz28+LuvwIcSvbv788XdlItLZ6vhkiHx0OHYX6fs6pa3NY5cffPufvxwApgZWEHFdTikRk0MbN32GwwtEO7sL9TgI8A57v7o/PvYf9JzwmwGTg8aV6cLyuQT5tyd38S+DjZFCzIRhG/6O4/cvctwNeBkQ8GlrCJ1uennXWqQMvP6e4/dPdteflaYPc8SNomYJO710aNryL7whhrzGx3si+Hy939s4PuT6+QDjdGWtwR0uIM6XCXGRcdhrlpTg133wl8Gnh9sr+R1+L5nJMGSIfHQ4dhfp+zqlrc0TnJ3SqPzr+fKqvFIzNo4u7/5O5L89e8HlozOwL4LPDL7v7t7vSw/6TnhOwfqDfn0a7PBB5z9wfjNjV/xNyndQVQi1D/eeAlZrbQzPYCXkD2T0RV+BZwrJkdZWZPAy4Arg7rXE0b57ACtDwXZnZwfo9gZmeQacWj7v4QcL+ZHZev+gqyYGljS36ePgpscPcPDro/vUQ63BhpcUdIizOkw11knHQYOtecvO2YWhl4LXBnXq+EFs9Fh0uQDo+HDkN756IhFdbidr6fjkm+n5aTBYx9tNJa7EMQjbbTF3Aw2SjYD4GteXnfvO1a4NC8/K68bQfZCNlH8uUfAX4ATOev1YP+TF04Jwb8E1m043XA6Ulbek7+PW+/HfgksE+y3u+TPey3k02nGvjn6vI5eg1ZFOf/Bv4oX/YbwG+0OodVe7VxLt4JrCeLmH0T8MJk26XAamAtmWHyrEF/ni6dk08BD5IF+tpENk05PScNdQd4Mdm0xbWJprxm0J+nD+dLOtz4vEiLW58jaXF750E6LB1u55y11ByyAbf/SjTn8kSvK6fFHehww++nvE06PAY63Oa5KLN3qqrFrc7Je/Lvp2ngm8CL8+WV1eJaqiAhhBBCCCGEEEIIkTAy7jlCCCGEEEIIIYQQ/USDJkIIIYQQQgghhBAN0KCJEEIIIYQQQgghRAM0aCKEEEIIIYQQQgjRAA2aCCGEEEIIIYQQQjRAgyaiMpjZ083sP8xswaD7AmBmV5jZsYPuhxBC9AvpsBBCDB5psRDdRYMmokr8KvBZd9/ZzsqW0ctn4MPAH/Rw/0IIMWxIh4UQYvBIi4XoIho0EUOPmT3fzNaa2Z5mtreZrTezkxus+ovA5/Nt9jGzr5rZGjNbZ2bn58uXmNkGM/sQsAY43Mz+IF/nNjP7QL7eu8zsjvy4V+TL9jazj5nZt8xsKtnnAjP763wfa83st/L+/CfwSjNb2NszJIQQvUU6LIQQg0daLMRgMHcfdB+EaImZ/W9gT+DpwCZ3/4vQ/jTgPnc/OK8vBPZy9x+a2QHATcCxwJHAd4AXuvtNZvZq4CLgle7+hJnt5+7fN7PNwFHu/qSZLXL3rWb258Ad7v5JM1sE3AIsA94MvBL4eXffUdtH3o8vA+9191t7fIqEEKKnSIeFEGLwSIuF6D8a7ROjwp8B3wK2A+9q0H4AsDWpG/DnZvbTwC7gMOCgvO277n5TXn4l8HF3fwKgJuzAWuByM1sFrMqX/Qxwnpn9Xl7fEzgi38c/u/uOsA+AR4BDAX1BCCFGHemwEEIMHmmxEH1GgyZiVNgP2AfYnUyYfxTaf5wvr/GLwLOB09z9KTPbmLSn2xrQaLrVucBPA+cBF5nZSfm6r3f3u9IVzazZPsiP+ePSTyaEEKOBdFgIIQaPtFiIPqOYJmJUuIRsyuDlwF/GRnf/AbDAzGpfAs8EHsm/HF5GNgWxEV8CftXM9gIws/0sC4R1uLt/jSxo1SKyL6frgd/KvxAws2XJPn6j5qdpZvsl+38usH5uH1kIIYYK6bAQQgweabEQfUaDJmLoMbM3Azvc/V+BDwDPN7OXN1j1S8CL8/LlwOlmtppshP3ORvt29y8CVwOrzWwa+D1gAfBJM1sHTAF/6+5bgZVko/przez2vA7wEeC+fPltwC/k/T4I+LG7PziPjy+EEANHOiyEEINHWizEYFAgWFEZ8lHu/+XuvzzovgCY2e8AP3T3jw66L0II0Q+kw0IIMXikxUJ0F800EZXB3aeAr5nZgkH3JWcrcNmgOyGEEP1COiyEEINHWixEd9FMEyGEEEIIIYQQQogGaKaJEEIIIYQQQgghRAM0aCKEEEIIIYQQQgjRAA2aCCGEEEIIIYQQQjRAgyZCCCGEEEIIIYQQDdCgiRBCCCGEEEIIIUQDNGgihBBCCCGEEEII0QANmgghhBBCCCGEEEI0QIMmQgghhBBCCCGEEA3QoIkQQgghhBBCCCFEAzRoIoQQQgghhBBCCNEADZoIIYQQQgghhBBCNECDJkIIIYQQQgghhBAN0KCJEEIIIYQQQgghRAM0aCKEEEIIIYQQQgjRAA2aCCGEEEIIIYQQQjRAgyZCCCGEEEIIIYQQDdCgiRBCCCGEEEIIIUQDNGgihBBCCCGEEEII0QANmgghhBBCCCGEEEI0QIMmQgghhBBCCCGEEA3QoIkQQgghhBBCCCFEAzRoIoQQQgghhBBCCNEADZoIIYQQQgghhBBCNECDJkIIIYQQQgghhBAN0KCJEEIIIYQQQgghRAM0aCKEEEIIIYQQQgjRAA2aCCGEEEIIIYQQQjRAgyZCCCGEEEIIIYQQDdCgiRBCCCGEEEIIIUQDNGgihBBCCCGEEEII0QANmgghhBBCCCGEEEI0QIMmYt6Y2R+Z2XozW2tm02b2gnz5R8zsxB4cb1uo/0p+3Gkz+4mZrcvLH2hjX281s11mdkqy7HYzW9Jiu2vNbNFcP0O+jz81MzezY5Jlv5MvO30++xZCjDdRJ4cJM1uS69xvJcv+0cze2mK7PzOzV3bh+DeY2X1mZsmyVcN8zoQQvcfMdub24+1m9m9mtlfJumeZ2QvncaxFZvY/k/qhZnZVi22WmNntTdqem9um95jZBjO70swOyu3cf2yyTUNbNt9u0sxuM7M7zOzaDj9ez8j1u85Glq6LXqNBEzEvzOyngAlgubufArwSuB/A3f+Hu9/R6z64+8fdfam7LwU2Ay/L6+9tcxebgD/q8JivcfetnfW0IeuAC5L6G4CenzMhhJgPZrZgnrt4BHi3mT2t3Q3c/U/c/SvzPG6NrcCLIPvxAhzSpf0KIUaXH+f248nAT4DfKFn3LGDOgybAImBm0MTdN7v7G+ayIzPbE7gG+LC7H+PuJwAfBp5dtl2JLftnwJfd/VR3PxFo156eE2a2sEu72op0XfQIDZqI+XIIsMXdnwRw9y3uvhmKo8Fm9jYz+3a+7F9qo95mdqmZ/YOZfcPMvmNmb8iX72NmXzWzNfnMkfM76ZRl/FX+b8E6M/v5ktUngZPM7LgG+3lTvv3tZvaXyfKNZnaAme1tZtfko/G3145jZqeZ2X+Y2a1mdr2ZNRPuVcD5+TbPAR4Dvpcc58NmttqymTwXh+P/pZndkr+OiTsWQoj839AbzOwqM7vTzC7P9fHVZnZlWO8LeflnzOybuf7+m5ntky/faGZ/YmY3Am80s3fl/0KuNbMr8nX2NrOPmdm3zGyqRLu/B3wVeEuDPi81s5vy/X7OzJ6VL780+Y74QHLsv86XPdvMPpMf+1tm9qKSU3MFswPWPwt8Njl+w+8fy/7lvdPMLsuPe5WV/BMthBhp/hM4xsz2y2csrM116RTLZiP/BvA7ls1MeUkz/bFsVvHHch3+jpm9K9//B4Cj8+3/ypJZJHn5P3MNWmOtZ7T8AvBNd/9CbYG7f83da7NSDjWzL5rZ3Wb2f2rr1GzZBvs7hOwPxdq+1ubrm2WzAu/Ibd9rE02e2ZeZnW5mN+TlMyyz8afy9+Py5W/Nv1++AHyp2XeHmT3dzK7Iz/+ngaeXnAfpuugd7q6XXnN+AfsA08C3gQ8BL03abgBOBw4FNgL7AbuTfRH9Y77OpcC/kQ3gnQjcky9fCOyblw8A7gEsr28r6c/GfP3XA18GFgAHAfcBhzRY/63APwJvBi7Ll90OLMn7fR/ZSP1C4N+BFQ2O8y/J/p6Zf8ZvAM/Ol/088LEGx/5T4PfIRP1kstkub6mdt3yd/fL3BfnyU5Lj/1FefjMwOeh7QS+99BqeV00nyf4NfQxYnOvsN4EX55p2H7B3vt6HgV/Kde3ryfL3AH+SlzcCf5AcYzOwR15elL//OfBLtWVk3w17h74tyXX2KODOXN/+EXhr3r6W/LuE7B/Pv8vLl5LNxtsPuCv5Tqgd+1+BF+flI4ANTc7NDcAL8uMsAL6U96l2zhp+/+TrOPCivO1jwO8N+lrrpZde3XkFDfg88JvA/wXely9/OTCdl/80ff6b6U++3jeAPXI9eZTMTlwC3J5sP1MH9gL2zMvHAqvjOqHfHwTe3eQzvRX4Dpl9uifwXeDwvG0jcECDbc4mm7XxNTLb9NB8+c8ya1sfmq/zhrgvMtv/hry8L7AwL78S+EzSr03M2rkNvzuA/0VuQwOnADvIbeTQ5xuQruvVw5dmmoh54e7bgNOAt5P9c/hpq/dLPwP4D3f/vrs/RTZIkrLK3Xd55spzUL7MgD83s7XAV4DDkrZ2eDHwKXff6e4PA/8BPL9k/X8FzjSzo5JlzycT/e+5+w7gcuCnw3brgFdaNuvjJe7+GHAc2SDIl81sGvhjsh8szaiNjK8APhfafs7M1gBTwElkA0s1PpW8/1TJ/oUQ480t7r7J3XeRDXIvyTXti8BrLZsafS7Zj4QzyXTmv3L9egtwZLKvTyfltcDlZvZLZIYswM8A7823vYHMSD+iUafc/V7gFrJ/SQEws2eSDYL8R77oMup194fAduAjZvazwBP58lcC/5gf+2pgXzN7RpNzshO4kWxQ++nuvjFpK/v+ud/d/ysvf5Lsu0YIUQ2enuvHarJB5Y+SPeP/H4C7/zuwf65TkTL9ucbdn3T3LWSuia3s2d2BfzGzdWQ283zjA37V3R9z9+1kLuBHlq3s7tcDzwH+BTgemDKzZ5Npcc223kz2Z2Irngn8Wz6L5m/JbNkaX3b37+flZt8dP02mtXg242VtybGk66JndMuHTIwx7r6TTOBuyAX+LWT/CNawBpulPNlg3V8km+Fxmrs/ZWYbyQR0dkWz95MZ+ngWz6TQ3OhAZvYO4Nfy6muSz7DDzP6G7F/VdvuNu3/bzE7L9/UXZvYlsoGP9e7e7kDGF4C/Ivsn4YeWx7DKB3B+D3i+u//AzC6leA68SVkIIVJSjd3J7Hf/p4F3AN8HvuXuj1smQF929zc12dePkvK5ZAbtecBFZnYSmW6+3t3varNvfw5cRTa7pS1yvT4DeAXZgPM7yf4B3g34KXf/cbq+mV1PZhyvdvf/kTRdQabXfxoOUfb9E7VW2itEdfhxtCfNrJEt2Oi5b6Y/0FyDm/E7wMPAqfl+t7dYfz3w0pL20uNH29iz+CrfJ/tD8V/NbJLZwetmmreD2bAPqa26Eviau78ud2u6IWlLv08afnfk568TnZWui56gmSZiXpjZcWZ2bLJoKdnUv5RbgJea2bPyfzRf38aunwk8kgvby2gwKu7uf+SzAWAjXwd+3swWJKPjt7j7P9W2yUfJUy4l+6egFjjr5rzfB1gW9PBNZDNWZjCzQ4En3P2TwF8Dy8mmjT/bsiC5mNnu+Y+JhuRfsO8B3h+a9iX7QnnMzA4CXh3afz55/2az/QshRBNuINOsX2N2BslNwIssj5NkZnuZ2XPjhma2G9kU768Bf0A2nXof4Hrgt2o/NMxsWVkH3P1Osn8+J/L6Y8APzOwl+Sq/TL3u7gM8092vBX6b7HsHsunY70zWW5rv8+xc89MBE8hcRf+C2Vl7Ncq+f46oaTvZd8KNZZ9PCDHyfJ3sBzdmdhZZHL8fAo8D6Uy2hvpTQtw+5ZnAg/nswF8mczcp41+BF5rZucnxzzGz57XYDoBoG5vZy2txPfLZMkeTzbz5OnBBblsfArws2c1GspnnULTznwk8kJffWtKNZt8d6fk/mcxFpwzpuugJmmki5ss+wP+1LEr1DjIfwbenK7j7A2b252SDEJvJDOTHWuz3cuALZraabDr5nR3263NkLiu3kY0Y/4G7P1S2gbv/xMz+Afj7vP6gmV1I5tNpwLXu/vmw2fOAvzKzXcBTwG/m+3kD8A/5FM6FwN+R/RPQ7NhXNFh2m5lN5dt9B/ivsMoeZnYz2eBns3+FhRCiIe6+M/8H8a3kAVnd/Xu5i+WnzGyPfNU/JvMvT1kAfDLXOAP+1t23mtlKMr1bmxu/G8kHREp4P5kLYo23AP+cG+3fAX4lrP8M4POWZYwwsn9lAd4F/FM+/XohmbHdNPuFuzvZYHek7PtnA/AWM/t/wN1ksWCEENXlT4GP57ryBLPBq78AXJUHFP0tOtefR83sv3K3leuAf0qaPwR8xszeSGaD/qjRPpJ9/djMJoC/M7O/I7NH1wLv7vCz1jiNzNWoNnvkI+7+rVwTX07mmv5tigPaFwMfNbM/JLP3a/wf4DIz+1+Uu/M0++74MLPnf5rsj9imSNdFr6gFUROip5jZPu6+LZ9p8jmyoE4xfodok3xa4em5f6wQQogek08tn/QsHakQQow1udv4pLtfNei+zBXpumgXueeIfvGneXCn24F7yVLtCiGEEEIIIYQQQ0vfBk0sy739SD4NrVH78Wb2TTN70sx+L1l+uJl9zcw2mNl6M5vrVDMxQNz993JfyePd/V2uKU7zwt2XaJaJ6BTpsBBzx9036t/I8aGRXprZfmb2ZTO7O39/VtJ2oZndY2Z3mdnZyfLTzGxd3vYPllHb92P58pvzf7xr20iLxdDj7m8d5VkmIF0X7dPPmSaXAueUtH+fzB8w+qHtAH7X3U8gS4X4DjObb+otIYQYRy5FOiyEEO1wKfV6+V6y9K3HAl/N6+R6eAFZOtVzgA9ZFkAesvgIbweOzV/n5Pv+KLDT3Y8hS8X6l8lxpMVCCDFE9G3QxN2/TvYl0Kz9EXf/FlnwonT5g+6+Ji8/Thas57Be9lUIIaqIdFgIIdqjiV6eD1yWly8DViTLr3D3J939XrKg+GfkGUb2dfdv5jNsPwGsyPf9QuAH+fZXAa+oZQ6RFgshxHAxUtlz8qmLyyhGZU7b385M5pbdT4MD+tU1IcTY8OAWd3926/WqSSsdzteRFgsheshW3J+wARz4IHd/EGYy7B2YLz+MLF13jU35sqfyclwOcHDejrvvMLPHgP2BtlxvZRMLIQbP+NjEIzNoYmb7AJ8BfjvPj16Hu18CXJKtf6iHzLdCCNEFLv5urXSMmT8xhz08CNe7e5mbzFDSjg6DtFgI0WsumSnNQ4fXA9vTnebaNRcaDeB4yfLaNjG+W1vx3mQTCyGGg/GxiUdi0MTMdif7crjc3T876P4IIQTAj4F3zGG7Px7Bv/ykw0KIYWQeOrzd3U/vcLOHzeyQfJbJIcAj+fJNwOHJeouBzfnyxQ2WAzxYazOzhcAzKXGfrCEtFkIMI1W3iYc+5XDu3/lRYIO7f3DQ/RFCiBoG7D6H16ghHRZCDCt91uGrgbfk5bcAn0+WX2Bme5jZUWQBX2/JXXkeN7Mzcx19c7LNV4Ba9p03AP/eKrOgtFgIMaxU3Sbu20wTM/sUcBZwgJltAt5Hfq7c/Z/N7GBgNbAvsMvMfhs4ETgF+GVgnZlN57v7Q3e/tl99F4PmacBtZK67U8DxZLNqlwLTSf2UvG7AqcBaskD2tbZY72Td+W77PEZgjFJ0iDEi0/VypMNiPlzExayjv+rZzeM8D1jJ+7p7UsTA6ZUON9HLDwBXmtnbgPuANwK4+3ozuxK4gyzDzTvcfWe+q98ky5bzdOA64Lpk34vMbEe+r4+Z2W9Ii0U5u2BGiYdVbWUTjyOjZhN3St8+m7u/qUX7QxSnMNa4kcY+oWJsuI0JrmQlV3IRMMnpTLCalXwmry9ngjWsZBUX5Vus5PN5232FtljvZN35bQvZl4kQg0M6LObDOrJfg68D1uTLauXrgEM7qHeybbeOs647p0GMCSV6+Yom678feH+D5auBk8NiabGYI+uYYFVPbNVO1pVNLMYNDfOJEWAZK8nmlawE4LxQP7dQL2ubz7rz2/aUuX98MbRUfSqiECmnQFMNPLfDeq/WLdtWKlxNpMNivDilZ7aqbGIxH6quxVWeRSOGmviYlN2Ka7iITGiz0eyrQ/2aUC9rm8+689l2LRpVrx5Vn4ooqsf7uHjO295GNmOjkQZuJnOebLfeybbdOs7CeX7+i+XaM5RIh8V4sbaHtqpsYjF3qq7F1iLm1Mii9GrDRvTBPIViXJJTgdvJ4pbcmm9zGpn5ewLZpOsVwCrgZ4ANybbDGNPkBOB6sv89J8mC6i+dw3Hk+zl8XHxrLePCEjP/4zns4dfg1jlkbRhJpMXDRRqXZL6qtphMsYc1pskaskGUc4FrgLPJvjnmchzFQxk2LsF9s4F0uB2kw8PGfOKSpEo8X2WTTSzmy/jYxBo0EX3ittwHkxCXpFY/gwluaThiPckZwC3Jvl5PZqrXeKr33e+Y28gGeDImSD9PLQZLu/UVaER+mJj9gjjKzOfyM+pXRuQLohtIi4eLFVzMDoozMw6l+ayOQ0N9TWFfw61MRRWG5dR/nnY/+0JglQZNhojZQRPpcGukw8NGtInL7UAo2pCjpcQgm7jKjI9NrKE60SeiD2aMS7KixDdyRdjX0p72tDsU/TXn4ycq38/hper+m6J6xLgkncYPifsaZmL/5hM7Zdg/6zgjHRajx/ziksR9DT+yiceBqmtxlV2PRM/pZHrhF4I/Y6yvKvGNXBWOO01xpskwsrZQ68wvdLLuXMFr0bTF4aPq/ptiNOgkFfAainFJHqCz+CEpw+6VvjbUr6H9+CeNzssKLlZq4yFEOiyGg/nYxNHuK48XUmTYlRhkE48HVdfiKn820XM6S3s2SS3NGMDhTHIUkywjM01PZpIleT2LaTI5E9PkZGAJzKx7Uv8+4px5Xv6emdSTGJPJz5hJjmAyEflifReTTM2cqwmmWMlU2+d4+L88q0NtVF2IQbKO9lMB11xsavpyHplJ+bn83fLyQuDVZOZnWj+Cojk6zKQqnJrTjT5f/OyHA1cze55qrj1KbTx8SIfFcDBfm/jIJnbhNAQbcrSUGGQTjwdV12INv4l50Nn0wiLPJzNDLX9/WlI/PX81alsOLOjJp+kuu5EJtZEN9ixl9r/IhUlbo/rSwp40bXF4qfpURDEadJoKOGUpc1eqYTcgUhVu9XniZ18a9qXUxsOLdFgMB/OxiZfSvjqNmhKDbOLxoOpaPApPmhhasrRn0xSn1DWvp0z1o4MjSv00xmnaPcdxQrroNQvn8BKim6ylqBNp+ZoG9bitqKeRa0/ZeU3LOqf9RzosBs98bGKpRnNkE48SVdbiUeqrGAipj+Y0Me3ZJPcxmaQ9az7Fbh2zI8zTjOZ0uTge2qusPZ1MYzwhn+JZS+O2C3Dk29kfqj4VUQwLu1jByqZe27Vkjp8li8WxmOq42AyKTlx7IHN7+mz+fjZAk/gna6lF8JIWdwvpsOgf3bKJ47ZS4ubIJh4Vqq7FGjQRLSj6aAKs5PMzvoOzYQUhM8VrgyHx/bRkn8sYDRebQVGbxgjFgLeNzu1tpNcg8/W8Wr6dfaLqQa/EsLCuELMkxtO4nqISH8msOU4b76KeVIVbvS+keP6hVfyTdejsdw/psOgf3bKJG9l2ojGyiUeFqmuxhthEC05p6rc9mmnPqkYnadx0fbpN1f03xbBQ1OFRTwVcNTpNbawr1F2kw6J/yCYebmQTD5Kqa7EGTUQL1jb125Z3/DDQia+nro8Qo0lRhxWnZLjoNP6JrpAQo4ps4uFGNrHoHVWeRSPaolVe+dRHcxJYnKcF7jTtWav4H/O5FdNxym7GGRmF8c8yX8/Ut/MassgHt6F89t2j6lMRRf+4iIubKvEUsIlizIwNKE7JsNAq/snZFGPOnMcqlrKqqQqv5H197f+oIx0W3aNfNrHoDbKJB0nVtbjKn020Reu88p15y4v+UubrWfTtBJTPvstUPeiV6B/roDRuyRSzOcfKPOX1BPefVvFPohIfRvPrvK4P/a0a0mHRPWQTjzayiQdJr7TYzD4GTACPuPvJoe33gL8Cnu3uW/JlFwJvA3YC73L36/PlpwGXAk8HrgXe7e7ebj80hDb2tM4rH9cXo0K9p738O7tLbVS9qunVRP84hVZxMIrritEhXq+y66xr2znSYdE9ZBNXF9nEvaaHWnwpcE7d8cwOB14F3JcsOxG4gGzq0DnAh8ysln3kw8DbgWPzV90+y9D3xlhQNt3wC1xEJhCZj99kqDfy0ezGyGs33Wh6lfq3V/vtF/We9uXX+gvAa9E0xfbRP5yifcrTBtf+/6o9jw+QzSyZfT5n6ZYKi/4QlTjV3UbXeYXSFXeEdFh0xjDaxKL3yCbuNb3SYnf/upktadD0t8AfAJ9Plp0PXOHuTwL3mtk9wBlmthHY192/CWBmnwBWkE34bAsNmowFraYb1qahARzOJEcW8srLR3NUqfe0L+az38UkUzPXPkvNNqVpih1Qdf9N0U3K0wZvzt9rz+N5ZCbZ5/L382HGM1sqPFoUvewz3ajFOzkcuJrZ674cpSvuFOmw6AzZxOOJbOJeMw8tPsDMVif1S9z9ktJjmZ0HPODut5lZ2nQYcFNS35Qveyovx+Vto2GysaDVdMOUpWRCUPPRXBjqumVGh5pvZ7NrubSwtqYpdk7V06uJbtJZ2uClzD6ty/K6VHg0SZU4XsulYV2lK+4c6bDoDNnE44ls4l4zDy3e4u6nJ69WAyZ7AX8E/EmTbkS8ZHnb6GkfC4op0urTbhXXFeOCUrPNFxnron2UNljUo3TF80c6LDpDNrFohGzi+dJHLT4aOAq4LXe7WQysMbODyWaQHJ6su5hsMu+mvByXt41mNFaCTlKkXQOcnUxJm2Z2lHVYpxqOemyRYWWuqdmmqb9nxnf8tdsiamZ7Al8H9sh3f5W7v8/M/hT4NeB7+ap/6O7XdvnwYl40j1uitMGiEd1MVzzO8U5kzIpZqm4Ti94gm7gb9EOL3X0dcGCtng+cnO7uW8zsauBfzeyDZN6txwK3uPtOM3vczM4EbgbeDPzfTo6r75lK0GmKtDRhZUzJJcaHuadmA1jJ58fet9OA3eeiojtKW58EXu7u28xsd+BGM6sFqvpbd//rORxR9IXyuCVKGywi3UxXPK7xTnqkw2JkkU0s5oJs4vnSKy02s08BZ5HFPtkEvM/dP9poXXdfb2ZXAnfke36Hu+/Mm3+T2ZTD19FBEFgY56GwSqEUaaLblKdmk29nhhksXNj5qwzP2JZXa7MXO/K7FIOiPG5JcU0hWtNJuuJxvat6ocNilJFNLLqNbOJ26JUWu/ub3P0Qd9/d3RfHARN3X+LuW5L6+939aHc/zt2vS5avdveT87Z3urtimowfrfwz5S0vOqVxarZpsntKvp0ZZrD7gs5f5JHCk9fbi/u1BWY2DTwCfNndb86b3mlma83sY2b2rL5+WNEG5XFLimsK0ZpG6YqnUbyTlHnosKgksolFt5FN3A5V12KNtY8sqc/mTiZZxiQT1PtnjkqKNMUtGS7KUrNlCVIb+3aOlz9nbVR9Dmxx99ObNeZTCZea2SLgc2Z2MvBhsj8yPH//G+BX53R00SWKMUxOYDb+RIxborTBYi6UpStO451sBi5kFRtYNXYxTuahw6IyVM0mFsOFbOJ2qLoWV/ijVZ3oswnylhfdo8zTfiFlvp3j7M/Zbdx9q5ndAJyTxjIxs3+hdqrFACnGMLkeecqL7tKZl71inIhxRTax6CWyicW4DH1VkuizWWwToneU+3aO0/1XC3rV6at0n2bPzmeYYGZPB14J3GlmhySrvQ64vTefSrRPfQyTYqsQvaNeicfTt74XOixGDdnEYlDIJq5RdS0eoa6KIpnP5kqa5ZXXqKboFY19O2fvxTG6/wzovj/mIcBlZraAbGD7SnefNLP/z8yWkrnnbAR+vetHFi15HxfPlG+Dwr2/Oaw7Rk+CGAD1Spz9t167H1fwZ4X772Le16+u9Zfe6LAYKWQTi0Ehm3iGimuxBk1Ghph3Ps0zPwksJpu8K/9M0Wuib2dZ/vqK+3MaXVdRd19LcSZ+bfkvd/dIYi4UPefhAYoxTOQpL/pFvRLPxjh5ADicbIS18vdiD3RYDDuyicWwIJt4hoprcYU/WtWozztf9KE7kuxuHZPRTDFAom9nef76SvtzVvwLQtSzDgpxTKYo95wXole0UuLDmL1P1/W9d31EOjyGyCYWw4Js4hkqrsUVHeqqIvV552O7EINhzP05F87hJUaWU4hxI4ptQgyKeP+l92nl703p8Jghm1gMK7KJq6rFI9TVcSNOPdwRfORi4owx8pkbGLuHutIkZ0R/zslwr64hk5oKTk2suP+mgIu4uKDEtf+PGvnOS4XFIIlKHGM8rODiggqvrEqME+nwGCCbWIwKsomrigZNhpY49XB57iNX43Cy6YfynheDJvpz7mKSqeRe3VzdqYkVn4oo6t1xNufvtft7OTCBVFgMnlSJvwAFm2E5xXTElXLXkQ6PAbKJxaggm7iqVGRoq4q0mnq4lOwhq/ls6lKKQVHz56zdi0tDe4WnJta+ICo6FVHUu+NEJZ5AKiyGg1SJXxvaYjriCqmwdHgskE0sRgXZxFXVYqnK0JKlT5umNp3rmrp2IYaTxunXpknTr1WIBXN4iZFhLRTuXymxGAUapSNO7+PK3bfS4Yojm1iMKrKJq6LFIzS+Mw7sRvbwLAVgktOY5HzgauAc4Ki8bZpsZDK90xRfo/s8PdRjTJP08flxj/sySpSlX5sk801OE2GO8Nhtxacijie7WMHKhmlclVZYjApl6Yhr9zFJjJNsoviIarF0uKIUE7xPsoxJJsgGTKTEYlSQTVwVKvzRRpG1TPAZVvKZxM/t1rztSLKc8yTvQgwj5enXJphiJVdXw5ez4l8Q48m6QgyT6ykmD1RaYTEKtE6CWYxxkv04HdE7WjpcUWIcE1CCdzF6yCauCiM8nFVFljZNZVnvEyfEqFD016ycL2eFpyKOJ6eUxjCpwB0rxpD6JJgV02LpcAWJcUyKbUKMJrKJR1WLNWgyVEwHP7dimxCjSdFfs9K+nKICrFUME1E5WsU40Z0tho8Yx6TYJsRoIpt4VKnwJJpRII1hMg2czCT3M8kK4HNkKdSeTzYd8VSKw3E7+tnRCvP0JmWoj2ESKXt8FONkltSfcw2TbM59Oa8h87S/jZHNV1/xqYjjgWKYiOrTKsbJhaxiA6tGM8aJdLgipDFMarEf7ktiPywmc0+XEotRRjbxqFLhjzYKxBgm9wO35C/IAr8asHxQHRSiC6T+nAuJnvUjna++4l8Q44FimIjqU+kYJ9LhihBjmNxH8S49ktk0rkKMKrKJR5URGr6qIjGGyYrQroCvomrUe9aPtD9nxXPSjweKYSLGj0rFOJEOV4QYw0RqLKqObOJR0uIR6moViO44zkVkD0bmy/b5sP5a4PS8rJTCs5Sl/u0kTXBcv5U7TrwGZS440X2qX9cv/QytHu9B9DH6a06GZ2ANWb9HaGriCAWxEhkXcfHMJPAvQOEefCCsu5aR+p9HiLaoV+LMEbj2HCzjz3gtsyq8kvf1t4OdIh0eQaI7zo5gD0yG9aXGomrIJh4lNGjSV6I7zvPzXN01jgCeA5xGlmpYM01E1Yie9buYZCp5BjaP1tTEik9FrCrroOCSk+rweWSTwBXDRFSZeiWGq5l9DiaYfT7W9b13HSIdHlGiO87yYBMfjtRYVBvZxKPEkA9XVY3ojvO60P4Cspkllr9XeLhOjCk1X86aX/LS0D5iUxMrPhWxqpxCdEWYZSnFO1RfkqKKtFLi9PkYchWWDo8srdxxliI1FtVGNvEoabEUqK/ElMKrQvut/e2OEAOnPhHmSKVfMyqdk76qrCWm+Su2CTFuxPs+fT6G/pmQDo8oMaWwEryLcUc28TAzQuM7o0ij9GnLk9RSrwaWkLnhTAEnMxtXolV8jVEnfr4Yi6QsFXCs71vSFm/xsvPaKmZJJzFM4nF7FS+k7NzEz9rq8w0ibk59Isxsem4txeAuwBlaX86KT0WsCmkMk5hu9QHgtSiRpRhvikkwszTEadptuLjgJDFUMU6kwyNCWUrha1CC90HQiU0seo9s4mGmwh9tGGiVPu0YZtMJK62wGEfKE2FOMMVKrh5uX06p6NATY5jEtMJKZCnGnfIkmMV0xEMZ46RHOmxmvwP8D7JfKuuAXwH2Aj5N9q/XRuDn3P0H+foXAm8DdgLvcvfr8+WnAZeS/bNxLfBud/fe9HpYaWUTK8G7GHdkEw8zQzZEVTVa+Wsq0KsQRYr+mkPvy1nxqYhVIcYwUSJLIZrTKh3x0D0vPdJhMzsMeBdwurufnG91AfBe4Kvufizw1byOmZ2Yt58EnAN8yMxqR/ow8Hbg2Px1zvw+9CiilMJCdIZs4mFCgyY9pZW/5lSf+yPEsFP01xx6X86KB72qCjGGiTznhWhOvVf9kMc46a0OLwSebmYLyWaYbAbOBy7L2y8DVuTl84Er3P1Jd78XuAc4w8wOAfZ192/ms0s+kWwzRiiGiRCdIZt4mBihro4Kqc/mTiZZxiQTNPbXPIliLIz0ckRfwhgzY9R8DaPf5H4d1A/qYN0Y4yMSz+MPm5QBvh/qj4d6eg1aXZ8dJW2dsG+oPyPUyz5/jGES+1wWs6VfFD3rJ9mc+DufQDZVcYjy1Vfcf3N02cUKVjaMYVKL0SDPeSEaU+9VX//8pDFOMmeLAWrx3HX4ADNbndQvcfdLahV3f8DM/hq4j+wL8kvu/iUzO8jdH8zXedDMDsw3OQy4KdnfpnzZU3k5Lh8DOrGJpcTtEe3pspu/VXzE+dijo/Y7ZBSRTQxgZh8DJoBH8ll/mNlfkYWk+wnw38CvuPvWvK0nbpIy97tO9NmE2Rkl0V9zhOYkCdEXyj3rRypfvRgg60pjmMhzXojmlHvVZ6QxTrIfxSP5JG1x99ObNZrZs8hmjxwFbAX+zcx+qWR/1mCZlywfAzqxiYUQRWQT51wK/CPZLL0aXwYudPcdZvaXwIXAe4Kb5KHAV8zsue6+k1k3yZvIBk3OAa5rtxNyz+k60Wez2CaEaJd6z/qh9Oes8FTE0eUUxTARoku0inEyFE9Ub3T4lcC97v49d3+KbLLNC4GHc5cb8vdH8vU3AYcn2y8mm5yzKS/H5WOAbGIhusP42sTu/nXC9H93/5K716bM38SsxvbMTVKDJl0n+mwW24QQ7TIC+eorHvRqdFmrGCZCdIlWMU4G/kT1TofvA840s73MzIBXABuAq4G35Ou8Bfh8Xr4auMDM9jCzo8gCvt6Su/I8bmZn5vt5c7JNxZFNLER3qLRNfICZrU5eb+/wyL/K7IyRw4D7k7aaO+RhzNNNUv95zpuyvPOTZANfy2jsr9mJP+Ao+g6mvpQHhrbFoX5kqC+ZLe65V/mmi5LynqFtW6hvCf6dD+0/W94R44VEYgyQvUraYnyQslgj8dpGH9Q0bkmrmCbptq1imAzq8S87F2kfa9mlGuWrHxJ/TsU0GRou4uIZJZ4i+zZUDBMh5k9ZjJMHgPNYxVJWzTxbK3lffzvYIx1295vN7Cqy+fA7yKTlEmAf4EozexvZwMob8/XXm9mVwB35+u/Ip4QD/CazvvTX0cGU8NFiPjaxmKUsbkm0oVrVU1rF3/txk3KjbWMfR/F3yihRr8QVsolLXSVLD2n2R2Q35+VJLyJdcZOUuT9vWuWdP5LsOlXWz0yIHrGAVp71A/fn1KDJ0LAOCnFMppDnvBDdoFWMk8OYfe7W9b139FSH3f19UDcK9CTZrJNG678feH+D5auBk7vewaFDNrEQvaF1tKlxs4nN7C1kAWJfkQR07ZmbpNxz5o3yzgvRH4bUn7PL08LNbE8zu8XMbjOz9WZ2cb58PzP7spndnb8/q1cfaRQ5hRhnodgmhOgO8XlKn7uBPWtykxwSZBML0R/GwyZuhpmdA7wHOM/dn0iaeuYmqf9I5800F5HdoJlP2WRoX0v7I32jPrUtTtVLXXKODW0nFKt7hrTCS5Ny/G9mSagvSsrbQ9tDoX5nSf2e2P/oChNTDi9sUob66ZFPlbR1Uo/uOMF1qekx2yE9TicpruN5i/2P5yZdv1Vq7ZSpUJ8Mz940sy49faI3o+pPAi93921mtjtwo5ldB/ws8FV3/4CZvRd4L9kXhiC7+mtI74dZOlFhIUQ50XM+1eGB/BOnGX9DRDdt4nGilR24X1Lev6QtbhttrGjHPhrqaazNMnusnXbRW6ISV9Ymxsw+BZxFFvtkE9nsvwuBPYAvZ2Mg3OTuv9FLN0l9zcyb6CJ1ONn0Q3nPC9FdTiFLuV57tnZRHEhp5K7YY3rwBZFPMaxF49k9fzlZRPCz8uWXATegQZMZ4tVfTjZnUyosRHdJPeu/ENoGoMIaNBkqZBML0R9ijJNq2sQA7v6mBos/WrJ+T9wk5Z4zb04N06GWko2i13w2dYqF6A61GCe1Z2tpePYG8O9V7Qui8/RqpZHCzWyBmU2TpbL8srvfDByUTy8kf4/RlceaUym6CUwgFRaiF9Q86w14LcXnbiBzCOauw6LryCYWoj+kSjzyNvFIIPWaNzGd2hCkfBJiLBiSZ29u/ptb3P305HVJukt33+nuS8kCVZ1hZmMQQHB+rKWYBlVKLETvGZrnTjFNhoQh+V4WYuwYkmevwlrct/GdPGDL35Odno+4+wdC+1lkAVnuzRd91t3/LG9bBHyEbEqNA7/q7t/sS8frKEundg1wUo+OG+NGRAYRD6UshgkU45icUWxaEmJxnBU2fXFSPrPYtM+S7xXqCxbunCk/tinERpkO09OiC+aWpBzjn2zrJDZHWXo4KI8XUrZfKMYxiTFM4rad+JjGz9fqHmt23E5jmqTElHaxHknv85PCszeAdGs9nhbu7lvN7AbgHOBhMzvE3R80s0PIZqF0TFW0OE0xHNOgbgZePYhOCTFmnETmFJ6m94aLCyrc8xTEI+ieUxUdHpxNPOqUxSyBYpIPgGOalIE9S9wwYpw/NrXoV1nK4VGPu1h1qm8TD5q+fDQzWwD8E/Aqsif2W2Z2tbvfEVb9T3efaLCLvwe+6O5vMLOnUR79sse0SqeWJrcUQvSO9Qw83VoPviDM7NnAU/mAydOBVwJ/SRYR/C3AB/L3jqJ+5/uujBbHFMPXIyUWot/UqzAcSp9TEI+YoV4lHZZNLMSwUE2beJjol3vOGcA97v4dd/8JcAVZUMOWmNm+wE+TB3xx95+4+9ZedbQ1SqcmxHAwpOnW5s8hwNfMbC3wLbKYJpNkgyWvMrO7yYztD5TsoxmV0eKYYlhKLET/qVfhIUlBPNxURodlEwsxLFTWJh4a+jVochhwf1LflC+L/JSZ3WZm15lZbU7fc4DvAR83sykz+4iZ7d3oIGb29lpgRXii0SpdIPqMXVPXLoToB/FZu2Yw/pxd9t9097XuvszdT3H3k2tTst39UXd/hbsfm79/v3xPDamMFsdYClJiIfpPvQoPKMbJaPnRV0aHZRMLMSxU0yYeJvo1iaaRw52H+hrgSHffZmavAVaRBcVYSJY98rfc/WYz+3vgvdTugXSHWTDFSwDMDo377xLRZ+xssumH3U6n1kl8iX6S9qsshgkU4pgcH2aPxgmnK4rV57xo/Uz5eeFBPyDklX88iflxx9EnFtpu3/784o7vCcfdJym3fBrmc03K4p9Eoq9rGTGGyVMlba0+4NOblKE8bklct1X/0379MLTFcYCyzxfTrZ3AJPTXl3r0piJWRosbxVLohRILIZpTr8L1sYVW9boT0uExsImrQGobhfh7LAn1EAdwcWJDLw2rHhDq25Ly7aHtzjg293Col9mqrWLOicZ08tthPnFjZBP3mn7NNNlElqy9xmKy79MZ3P2H7r4tL18L7G5mB+TbbspTbgJcRfaFMSDWM8EapljJBGuADSidmhCDIKZb2xCezfWlW3eF0UuvVhktXk8WO+Fn83cpsRD9p16Fi89lH1RYOiybWAghm7jn9EvNvgUca2ZH5UGrLiALajiDmR1sZpaXz8j79qi7PwTcb2bH5au+AojBsvpI9N+Uj5gQw8EAnk1j1KYiVkaLY0wTKbEQg2cgz6V0WDaxECIgm7jb9GV8x913mNk7yWZtLgA+5u7rzew38vZ/Bt4A/KaZ7SCbA3aBu9emE/4WcHn+5fId4Ff60e9Z0pRqX+Aishswmws5DSzr0nHSKVytLk0nqWXnQ1la4SWhLfzZcUwynXBFWPWXitWXnvTFQv1lfG2mfDT/XWhbED77Ro6aKT9eSM8Lty8KU0MX7sncKZs2N58pdZ08hq38ktNz06pPzwj1fZuUO123lXtOOsWzbGoo1H+Gss90c3g2vwC8lp6mWhuxqYijrcW7WMHKmYmntRjxteu9G91TYiHE3Jim+FwCrEhSEK+aeVq7iHS4ojbxqFOWVjikDeZFxeqZwYMrdWlfGjaN937qkrM1tN1Zkp5YlNCJi00rMWpl57bb1opp2cRdpm8fLZ9eeG1Y9s9J+R+Bf2yy7TRwei/7V05MqVZL3QSNXVOFEP3HCs/mBFOsZKr3qdZG7AtidLV4XSHF8Ob8vXa920o9IYToKUbRRlpOMQVx9mO7B1osHe4jsomFGH5kE3cbORu2RZzilKL880IMB8V/t/oyLbHi/pvDRVGHY2JLKbEQgyc+hzEFcU+0WDrcZ2QTCzH8FJ9F2cTzR4MmbRFTqhXbhBDDwHSh1pdUaxX33xwu1irFsBBDTqsUxD15UqXDfUY2sRDDT/FZlE08f0ZofGeQpCnVJskCnS9j/unUol/bwpK2+fi1dUKZDyZkn73GCcWmA0Jsi1cm5RXFphjD5LxiDDReyDdmygeGuBePhvxqaX1nfPq2hhgmW4vVQmq2baGtLr1aWT3GmOkk5kyrbcse07LjxO3itY2xSA5qUm5QT0/rorDqPqEeu7glube3xVgp8T6P57zs874AeBpwGnAzk9zLJOeRxdfrUaq1ivtvDhcnsZlVSjEsxBDTKgVxT7RYOtxnemUTjzqtbKwlSTnEAIwxTELcP1ZsnykefFgh0RKPPFy0z3Zt2nu2UmcyxczT0cZ6qkl5HCmLM9nJ77dW4lT2WyLSyTVJ1XgNk2zuffrhimuxZpq0RZpSbYpsWEzp1IQYLhaQuXlnqj3Baqb4EyZYTU9TrVV4KuJwsV4phoUYclqlIO6ZFkuH+4hsYiGGn1SNF/Yv/XCFtVjq1hZKqSbEaHFa//w3KzwVcbgo6rBUWIjhJ6Yg7llME+lwH5FNLMRo0adntuJarEGTtoj+m/LZFGK4ubV//psVHlUfLoo6LBUWYvhZS59imkiH+4hsYiFGiz49sxXX4hHqaj9Jc9BnXrmz/pvz8QVr5efWSR7wbvkaxmOWxbmAQm75hYuLTWeGVc+aLT7nBcWpYGnMEoDTuLVQP5r/jh2dYXM+ybfGwxw4U7634DMK3BM23hTqDyXlOlfCH4b646H+RFIu8wudL3HfZaT3VCcxTKDgb7tw36ZNdfWDQ1sII8P2UE+vwe3Bj3dLPFAn5zW9XsuZ5O4kpskJwG0UI2BozHi42cUKVjaNjfDqQXZNCNEWJwHXMfvcXsgqNrBq5rnOktZKi4ebXtnEVSC1oaPNdWCoJ/bz4r2KTa8sVjmnGHvk5MPWNe3BQ9sPLy5IbayNce0HQj3atZ3E6qtazJOy32jx2pbZ163WLYuP+P3Q1uoct3sNTgrPrGziuaBBk4bEHPT3AWvyF2ThB5VWTYjh5TZgdf4C2MUEa5JnGrryDFc86NVgWccO4HVkyns9UmEhRo31FJ9byGKbvG5m2Trm/SRLh3uMbGIhRpt6JZZN3DkaVmpI9P06t65dCDHMnB7q5/bOn7PC/puDpajDUmEhRo/4nJ5Lj2KcSId7iGxiIUabeiWWTdw5GjRpSPT9uqauXQgxzKwO9Wt6489Zcf/NwVLUYamwEKNHfE6voQcxTqTDPUY2sRCjTb0SyybunBHqaj+Jvl9nk00/TH2/5sJ8YphE4rZz9S18RqhHH8zDQj3xyTw+NC0N9dNnfTKP465C0+HcF3pR9Kv8CU+bKT8cYm+sC+f/Fl4wU777rjC9LP52vjPUCzFOHg2N0bewLMZJJ3FHIvHatcrTnhLvqYUlbTGmSbi2eybtS8OqJ5fUQ2ibupgmW0L9dpqzOvj5blsSVkjPTTxvaf1FZNdkKdnXwnOZhO77YFd8KuJgOYnNrJqJhdAtFRZC9I/ac9osNlFXtFg63GN6ZROPItH2TuNVtIobd9RsMdrPob7nAT8o1J9g1jba/NghxZVvCH26ISlvDMepWxDt3tSWrVrMkkirOJPptd0vtMVrm9bjuvE48bfEww17lxGvQdlvjbLrVa/Esok7RzNNGrI+5LPewGyua+WhF2L4WQAsI3tmlwEbepOjvuKj6oNlPYcCP0sWA0EqLMTosRvF53YDFJ7rrmixdLjHyCYWYrSpV2LZxJ0jpWuIctALUS16+ExX2H9zsBSvmVRYiNHnFBTTZPSQTSxEtZBNPBdGaHynl8R0aju4iOxGyny9psn+rZ4vrdwu5uOu0wnpFMJWU86WFKuLkhSxxxSb4qrPXDw75Sy63+wMt95mDmla/zbHFdq+zksK9a88nORq+2LoUyv3nB1p2uCYiu2RUI9T6n7cpNwpnbjnlLnjQPlU0eh6tX+xujQpvziselaxuvuZs+fioP2LUwsXsLNQ3/xoMUX0Uwck/YofdWuoT4c+FlymWqWETs/NLeGZXpO3zzPdWsWnIvabi7h4Rom/AIVrthvdUWEhxOCYJlPf2nO9jD/jtcyq8Ere1/lOpcNdpl828ShSZnNFd/dgT6euy4vCqttD9c6ibf6d1FaPdu1kqN+QVmKsivmkHK66u05MDZxegyWh7dhQT671wS0O81D8nZXa6vGcx98WrdJAt8u0bOI5oJkmQC2d2hR/xgSrgM1Mkn0lZFpkJdsKIYYfC8/05vDMr5vrbis9FbHfrIOZNMOHQeGaSYWFGH2M4nN9GNnzvoM5q7B0uOvIJhai2sgmngsaNAFap1NT/nkhRpv4DHcx3VqFpyL2m/qp+7NIhYUYfeJz3DUXPOlwF5FNLES1kU08FzRoAiidmhBVp0fp1io+qt5v1hLTkRbbhBCjTXyO0+d9zs+4dLjLyCYWotrIJp4LI9TVXjIK6dTmE++kzE8v+tbFFMMhpkSaXjb67S1q3oOtofEunluob6YY9+LR5LgxxfA3v1uMacKq5NzEmCY3xY48ERZsTMox7VerFMRlvp9lj1YrH8Qyv9G437L0dzFezZJiNaa8OzMpTxSbnvPSYmTtZUzNlA/JE0fW+Al7FOr37H90oX7LWbMpordteXbxQJsor29J79d4PWI9PTc9SrdWcf/NfnMScB1ZOtIHgNeSTR8dFhUWQsyPVInXkKUdrqUffjWwai47lQ53mVGwiftFtLE6STkcbe+ELaE+HeobQ/2hknVjjJPCD967Q1u0k2JsuNQ+rVoMk7JrB/XXL/3BE2ZeLA7rnt5kM6g3+W8P9ZsSu3ZHvB5lsRSh/BoNIAVxj7TYzD5G9svkEXc/OV+2H/Bpsh83G4Gfc/cf5G0XAm8DdgLvcvfr8+WnAZeSXfxrgXe7u7fbD800AZROTYiq06N0axUfVe8365lNR3oY2axNqbAQ1SFV4oUU0w/POemldLjLyCYWotqMnE18KXBOWPZe4Kvufizw1byOmZ0IXEA28nMO8CEzqzkBfRh4O1kk32Mb7LMUKR+gdGpCjBt65oeRGNNEV0WI6qLnfVjR96MQ48VwP/Pu/nXqp0mdD1yWly8DViTLr3D3J939XuAe4AwzOwTY192/mc8u+USyTVto0ASo99+Uv6YQ1aZ7z7wv6PwlGhNjmkiJhagu3XzepcPdRDaxEOPFwG3iA8xsdfJ6exuHOsjdHwTI3w/Mlx8G3J+stylfdhhFp//a8rbRBEWg3n9zjr5cLWnlHzifuCXppYz7ibnj01gX8X4JzngH0Ly+Z2gLfnuPPTQbl+SuI48rtD0cYqnsDOGT73/y8Nn93BSCp9xA83qMYbI9+gNG/85HCr0qUhbDBObuS9hN4iO8V1IOMU32CWkCTw6bvni2GGOYnMfVhfoZ3DxTPqhwDuHxcL9Ns7RQ/8neszFPblz6qmIf7qS8viX1I43+qPFclMlbd555N9jZZRU1s8PJRsAPBnYBl7j735vZnwK/BnwvX/UP3f3a7h59sKQxTWoxDoQQ1aTR875qDvvphQ6PN/2yiYeRVnEvyupx22AHbk/KdbZNqG8P9TSmybZo124I9Y1JOdq10Y6NATeqFsekjCgaMQ7gMbPFGMPkDWHV1MljSWiL1/aGUE+v9eq4cSe/S1rFSyxj4DbxFnc/vfVqbdEoJ7qXLG8bzTQB6v035+xZK4QYCbr0zOdfEJ2+WrAD+F13P4EsPO87ch9NgL9196X5q1IDJlCMaTKvGAdCiKGna897b3R4jJFNLMR4MdQ2cTMezl1uyN9r/+BuAg5P1ltMNi6/ieLMgNryttGgCTDsvlxCiG7TnWfeDXYs2K3jV+k+3R909zV5+XGyv5E6mkI4qijGgRDjQ7ee917o8Hgjm1iI8WJ4beISrgbekpffAnw+WX6Bme1hZkeRBXy9JXfhedzMzjQzA96cbNMWYzrWvgtYx2yqpR1cRHajZL5c02SJLgfJfC5NqzRaBzUpAwvD9MLonrNPyWHjFLTbZ/f13S3F/LbfbbXtPUk5plOL9UL6rpijdmOol011a+WOMwy0mjqausaEaxvToAX3nN3Pmp3y+TK+Vmh7DcVJDS988hsz5b0f2VVo+9GBRQHca49imufUNeuOk08stH1/cRgbiPdfgfjZy1IDxrZbwjP/BbIEt7V0iu2JuJuxc+FcntWfHGBm6Z18ibtfEtcysyVkYnQz8CLgnWb2ZrKn4Hdr6dVGlYu4uKDEa/JX7brsxuCVWAjRG6YpPu8AK7i4kNh2Je9ruZ956PActqkio2AT95LUPmjl5tuJG/DjoZ7Ynw8Ft/mHoo0SXXAeaFIO+wWKtmwnbuVVJ16rst9GUDCaXxma3lqsnnHqf8yUDy+E04D7jzu8UL9ln5cWN05/ttwZ7oNtrdJYd+tn/PSgbeLSVjP7FHAWWeyTTcD7gA8AV5rZ24D7gDcCuPt6M7sSuINs5vY73H1nvqvfZDbl8HX5q23GdNBkHROsYiWruAiYZHmen7pGI7cnIUR1sMIzP8EUK5nK9QCyFGztsXPBnCIKtvTfNLN9gM8Av+3uPzSzD5N9p3n+/jfAr87l4MPCOrJvtNeR/XjanL/Xrsv5A+qXEKL3GBR0eDmZm05ND9Z1sK856rAAZBMLMe4M3CYuxd3f1KTpFU3Wfz/w/gbLV1Mf0bFtxnR+YpyGdG5ob//mEEKMIsVnfK7TEh1jJws6frXCzHYnGzC53N0/C+DuD7v7TnffBfwLcEbbHR1S4vR8KbEQ40N8vs9lbu46vdLh8UE2sRDjzXDbxMPCmA6axNRK19S1CyGqTPEZn2uqNcfYwYKOX2XkvpYfBTa4+weT5Yckq72O4Jg2isSUo1JiIcaH+Hxfw9xSEPdCh8cL2cRCjDfDaxMPE2PqnhNTK50NHAEFT9peEH0Wu3n6031Fn7eYRuvApLx/samTGCbbQj2GE9malONHbbVtGtMk/jTc+kRYkK68MbTFOCXRTzT1OR0FX894IuM9lfo/7lVsahHTZNn+UzPlF/KNQttZj91YPOqXkkox4zB7H1WMcbL0NdOF+h3MxjE5dEExcPX3Dw4xTRYVq4U019tbnYsyas/4KcAaJtk851RrO7svoy8CfhlYZ2bT+bI/BN5kZkvJ3HM2Ar/e7QP3m5hytF9KLIQYPKkKrwVOAK5nbimIe6DDY8SgbOJhpCw2GtTb1ykxfki0N9P2eL/GbWM8lEdL2uJx0tSzo2DX9pKy2HbxWgb7c2lSnig2vfTULxbqb+CqmXKMaZLavABPnFq0zW9f+vzZyldCl+6Jv996pXNDbRMPDdX9ZKVkqZVWsib31zqC2alJmoYoRPXZjdlnfWGJHpRTm4rYTdz9Rho7kVc6xfAUWZogKbEQ40GqwqcCt1HUg3aTXvZCh8cL2cRCjDfDaxMPE2PqnqN0akKIGnPXg6r7b/YapRgWQtSYqx5Ih+eLbGIhRA3ZxM0Y00GT6L8pf00hxpf56UGVvyB6TYxpIiUWYnyZjx5Ih+eDbGIhRA3ZxM0YU/ec6L/Zmb/W3GkVg6GTy1G2r+inF/N8J3FM9gxNi0K9LKbJ1lCPcUp2lLRtCfUY0+ShtHJvaLwn1NMc9TGGSfT9HEX/zk58MpN6vLYxpskxxepz+fZMeSnTxR78V9j2P5JyPOWPFqvPfk7x4h9+/Ky/5/5x5UVhX/EzlD4i8do+VdKWMig9EDGmyasH2x0hxABppAerBtmhsWHcvgPLbO9oZEQbqyx2WrQzonGUEmOYxG2j7frjJuVG24rGxGsbfxsdVKweP1vc/axi3Jizub5Qfx2fmykf9kjxuh96YDF2X4xxcvsxSUyTg0OX7unk/usW46YH7TOmM00y/80pVjLBGtr3nBVCVI+560HVI4X3mjSmyaFIiYUYZ+aqB73UYTNbZGZXmdmdZrbBzH7KzPYzsy+b2d35+7OS9S80s3vM7C4zOztZfpqZrcvb/iHPkjYkyCYWQtSQTdyMMR00kf+mEKLGfP03F3b8EhmKaSKEqDG/mCY90+G/B77o7seTRUPcALwX+Kq7Hwt8Na9jZicCF5D9NXsO8CEzq/0i+DDwduDY/HVOux3oPbKJhRA1ZBM3Y0wHTeS/KYSoIf/NQaGYJkKIGsMW08TM9gV+GvgogLv/xN23AucDl+WrXQasyMvnA1e4+5Pufi+ZL/EZZnYIsK+7f9PdHfhEss0QIJtYCFFDNnEzRmd4p6v00l8r9TfrNIZJ2t6pj2Lq9xb99A4M9aT9gNC0KNRjTImUraEe45ak7TGGSawX4pIAbGxSbrRu6vtZRd/OMn/bkhgni0JTuNa7Ly76aB7KrN/lkhhHZkPY1+1J+THKua9Y3f/42Tgmz4h+ux3FMNkR6vHax/ZmzF0Pqp5erdcopokQosZcY5rMQ4cPMLPVSf0Sd78kqT8H+B7wcTM7FbgVeDdwkLs/CODuD5pZzcg6DLgp2X5TvuwpipHbasuHhHGPYVAWE7CTn0mtYo2kNklct5Ntq2jn9or0+rWKCRjal8wWT9z/jkLTC7i5UD/sP5I4JvcXmnj+z9xeqB934F3FFdJ4g3VxJPsRwyQim7gZYzrTRP6bQoga8/HfpNL+m71GMU2EEDXmHtNkzjq8xd1PT16XhF0vBJYDH3b3ZcCPyF1xmtAoTomXLB8SZBMLIWrIJm7GmA6ayH9TCFFjPnpQbf/NXqOYJkKIGnPXg57p8CZgk7vX/la+imwQ5eHc5Yb8/ZFk/cOT7ReTTZrZRPH/5NryIUE2sRCihmziZoxOT+fNLmAd2cX/AheR3QyZv9Y0sKxk206mR3UyFWw+lO17v9C2f7GaTv/q1D1ne1KO7jgPldS3x+mEG1vUH2hShvGemtjKxask5XCY9veMRUXXmEWJP9V+j2wvrvxg2Fdq7kX3nHj7hazCe/HETHkPnqSU6GFT6Far6a0pZc/wrUEPbic7r8uAKeA4mo0vV30qYvfZxQpWcgqZl+ya/FU797tRrsRCiOoyTVEPAFZw8YxerJpRiSK90mF3f8jM7jez49z9LuAVwB356y3AB/L3z+ebXA38q5l9kGyyzLHALe6+08weN7MzgZuBNwP/t+sd7oj52MSjRiu7qcxub7VtmdtMmY0S21q5G4v50yKddHSNSdL/HhTCAtS5sKceOKGJo4rVgw4s7mu3RT+aKe9auDeDZzrowRqyc1dT4ucxrjbxGA2arGOCVazMv3Yn81fGEGV+E0L0GQt6cD8TfIuVXJlrxQqypAmNqfIXRPdZxw7gdWRfw5vz99q5P39Q3RJCDByjaJstJxt5qOlF9iO/sRb3UId/C7jczJ4GfAf4FbJfDFea2dvIona9EcDd15vZlWSDKjuAd7j7znw/vwlcSvZL7br8NUBkEwshGhFt4s1MsKagFeNqE7d0zzGz3cxsmZmda2YvN7OD+tGx7hOnG6U0v/hCiKoT/1F7XdtTE2uj6v2IFF4NLS7q8LmhVUosxPgSn/9zoS0t7qUOu/t0Hu/kFHdfQTa38gjgg8CvA7/g7t9P1n+/ux/t7se5+3XJ8tXufnLe9s48i84AkU0shGhEvRIPo008CJrONDGzo4H3AK8E7iaLIL4n8FwzewL4f8Bl7r6rHx2dP2vDdKNim74khBhXpkN9VdCK5vrgWM+DWFVLi4s6HJ36pcRCjC8xseU1ZA6SrbRYOjwXZBMLIRpRr8TDZBMPkjL3nP8NfBj49TginqdX+wXgl5nNVT/kpCmUJsnicC0ju/jLoHCRy05Lq/gmZb6RrbyhUp/GVseJ8VHSNMMxqERIQZz46bWMaRIpi2kS0wgX4pjcHRq/G+obQz2mFR5nyu6FEv/bMrddYMGCnYV6Ib5IDDWyvXn9iR8Vm/Yq7raOdFT5SfYoP068xwpuv618glPKUjW/gOx5Oh1YDZzMJLsxyeuBz9Aq3VofglhVSItPYjOrZlKKnk32l23qKSuEGE9qz39ND04Armc2BXGZFkuHO6XMJq66EpfFBIy2datYhDualFvVFcNk6CixmZ+exOIDeEY0TtPYftFuDTbyAopG8q4dye/POjM23hdlqai7Rb0SZ+467aUgHqXArp3S9JO5+5tK2h4B/q4XHeod63OfrDW5T9aRZH6bp0KFR8WEEK1YADw/Lz8f+BYT3MRKbsq14mDKRtV7PbWwWlq8fial6BSwgdkzq/81hRhvdqOoB7dBQS/WsJ5mM02kw51SZhMLIcaXeiUuasURDNImHiTtxDR5h5ktSurPMrP/2dNe9QSlVBNCtMPpQ6kV1dDiog4Px5kVQgwjMQXxMChGNXQYZBMLIdpDWlGj5aAJ8GvuvrVWcfcfAL/Wsx71jMx/c5rUJ0sIISKr29aKPge9qoAWF3VYKiyEaMZaaEuLpcNzQTaxEKId2teKsQ0Em7CbmVnNh9PMFgBP6223ekHqv3kd2UhZza+xlc9iGWWxEsraoN43LV0/+qnFPj4j1A9MyocVm2LckoOblKE+T3mMMbGjpC368RXikjwS2jaVrCua0z1fwZ07i0K1Y0FSD6FG2DPUk1TydTFMQggdnlmsbk0C52yNQXS2hm3r7qnUlTzGNCkjPj9lcWJOZ5LvMMkbgX+jlf9mH4NeVUCLizFNXj3o7gghhpaTyKy1dmKaSIc7JbWJW8cpGD3KbPGyuCWtYppE2zy1Q6JNH22UsnXFwCkJQfNj9io0PR5+LD37mYmxun/Yz6HF6sOExIdbEgN7a+xUJ7H7enVPdaYVVQ4E285Mk+vJ8tG/wsxeDnwK+GJvu9ULMv/NKVYywWpg/aA7JIQYSqaY4BtM8TtM8A3KtCIbVV/Y8WuOVECLZ2OaHIpUWAjRnPVQ0ItmiiEdngupTbwGqbEQojHta0WftbjvtDNo8h7g34HfBN4BfBX4g152qjdEn6ylA+yLEGJ4OWNYc9JXQIsV00QI0R7txjSRDs8FxSkQQrRD+1ox9u457r7LzC4F/t3d7+p9l3rFupBn+jayFKPdpizlcKTMXafVFME4/yuZ7rWwOI2MxTSvR/eceEfENMIppa4TAN9PytH95vGSHYsinYzCJvdQnMUXrtcT24r31NZnPmum/P0Di/44+x0SfLHS6YbB/YbnlNc3Jxs/XHAro95rq+7+S++pVqnY5pr++7+CVkyTpWJsTL8EvxpaPF04t7tRdmaFEOPMNLCG9rRYOtwp0x19z40e6Xd8K3t63ybluB8odwvuRfpX0TuCzRhDDiT2Z3Sp2chRhfpzlj40Wzm8uJsHTt6vUL+DE4sr3JmUow1csHmh/v7rh5tXZ1oxSoMgndJO9pzzyM7QF/P6UjO7usf96gE26A4IIUaCqBXNtcMxdrCg49ecelUJLbaSmhBCzFKvD40VQzo8F9r/nhNCjDPDaRMPgnbcc94HnEEensbdp4ElPetRz1gWphedNsjOCCGGlp8KWtE4Hz303X+zAlp8auHcNj+zQohx51Sie05jxZAOz4VT2/6eE0KMM+1rhWKawA53f6znPek50yFl0tQgOyOEGFpu6igVYx/9NyugxUo5LIRoj3ZTDoN0uHOUclgI0Q6daUUvtNjMfsfM1pvZ7Wb2KTPb08z2M7Mvm9nd+fuzkvUvNLN7zOwuMzt7Pp8+pZ3hndvN7BeABWZ2LPAu4Bvd6kD/OIlJvssk5wGTwMlJWyc+Ya3ilJT5NHYS4yT6XO4X6iGtcBqoJMYwWdJ81bp0xLH7W0N9e5MyUO9798OkHP3wfohoRtl9Ei9QSYq7GHMm1LdveVah/ugzZ+PkPBhypO137HeKG6fujPE+CKGCHji+uT/nxkeLfqGtY5qk91E8F/G8pe2djGSfxiR3M8kFwBWUpVerBb3qExXQYqUcFkK0R7sph6XDc6FqKYfj93+ZPf2MUN+3pK0sxTCU/35oFXdN9JcW1zI2J2FKNj65pNC0eo+it8LRL7pnprwHPym0fYVXFurf4IXF46xOyh3HNOlHHJ32taIXWmxmh5Hp7Inu/mMzuxK4ADgR+Kq7f8DM3gu8F3iPmZ2Yt59EFoHxK2b2XHffOd++tDPT5LfyAz9Jllrth8Bvz/fA/Wc9E6xmij9hgluA2wfdISHEUHIrE9zIFO9kghtpnV6tb/9wVkCLlXJYCNEenaUclg53hlIOCyHaodOUwz3R4oXA081sIbAX2Tj6+cBleftlwIq8fD5whbs/6e73AveQuVTOm5aDJu7+hLv/kbs/H3gB8JfuXjfHYPhZGnyyqhQlXAjRPc7sKBVjv4JeVUOLlXJYCNEe7aYcBulw5yjlsBCiHTrTijlq8QFmtjp5vb22P3d/APhr4D7gQeAxd/8ScJC7P5iv8yDMpOM8DLg/6dIm6t0z5kQ72XP+1cz2NbO9yYaX7jKz3+/GwfuLYpoIIdqhs5gm3cbMDjezr5nZhtyH89358qvM7N/N7B6yfze/PXparJgmQoj26CSmSb+ojk2smCZCiHboi1ZscffTk9cltYY8Vsn5wFFkkw73NrNfKtlXo/Q+3o1OtuPof6K7/9DMfhG4FngPcCvwV93oQP84nkmWJz5Zx1Keb32ulMWjaOXPmPpdxlzxB4V6GDRblJSXhFVjPY1psk9o29q4ZzOk/6fEmBl15/PxkjbRPqnPYkkMk1iP1yfGB3moqCv3Hz2bXP4ejim0nXR6iGnyZKN+5pxZrK4OQU7W8byZ8lPT4T7fGPZVF9MkjYXTib9w9Pssk77TmOQuJnkj8G+0sbvyngAAWYpJREFU9t/seuTvHcDvuvsaM3sGcKuZfRl4CfC3ZCPovwHcBPwyI6XFimkihGiPzmKa9C0DQ0Vs4lGPaRJt7Xj9y+zp/UM9jbsW99OJ7RptkH7EmxDldGI/h1iLG2fvm8dWH1xo+s8X/XShvlfJffIVXlGof+e/wrOWxjTh0fI+DSQuTqcxTbquxa8E7nX37wGY2WeBFwIPm9kh7v6gmR0CPJKvvwk4PNl+MbWvkHnSTkyT3c1sdzJfoc+7+1N0acSmv8h/UwjRDrcywTeY4neY4Bv023/T3R909zV5+XFgA9ko6SLgcjIt/iBwHiOnxYppIoRojyGNaSKbWAgxRgw8psl9wJlmtpeZGfAKMrv4auAt+TpvAT6fl68GLjCzPczsKLJZErfM+eMntDNo8v/I/v/dG/i6mR1Jl1OfmNk5eVqge/IIuLHdzOwf8va1Zra886PIf1MI0Q6dxTSZ4xdEU//NFDNbQhaA6WZgJ9nskr2BVcDBdFGLB6HDUmEhRDM6iWnSx0ET2cRCiDGiM63owR+JNwNXAWuAdWRjF5cAHwBeZWZ3A6/K67j7euBK4A7gi8A7upE5B9pwz3H3fwD+oVY3s/uAl3Xj4Pn+FgD/RPaBNwHfMrOr3f2OZLVXk40UHUsWeOvD+XsHZD5ZK0l9sk6dX+eFEBXkpra1wrG5BhTc4u6nl61gZvsAnwF+O58O/hN3Pyxp30WXtHhQOrwQqbAQojFryazkVlo8Dx3uGNnEQojxon2t6JUWu/v7gPeFxU9C8H2aXf/9wPu73Y+WgyZm9ufA/3H3rfmiRcDvAn/cpT6cAdzj7t/Jj3cFWcCX9AvifOAT7u7ATWa2qObH1P5huuW/2cpHMT2l0fesEx/MmCs+xjTZq1hdkpSL4SjqY5ocTHNiHIwYEz6t152K+GfLE0lZ+em7Q4s88+k1iNcuxgcJ+eDvT1wA7+K5hbb1h95TqJ/4itkYJzuCPk4/8+RCPeakn0ozV8XM3xtDfUec9Zx+3vncU2XP8alMciaTvJ5szKLv/pvk078/A1zu7p/NF+80s+Pc/a7cf3ML8Kd0R4v7psOKaSKEaIdhjGkim3hYKLOfYz3GNNkv1KO9nRJthbK6YpgMN63s5xBPJIlpUow7Av95+ksK9Z/s8bSZ8pPsUVz34eK6TIbDTqeVB0Jj/F01iHts4DFNhoZ23HNenXw54O4/AF7TxT60kxqoC+mD5L8phGiHKSa4iSl+nwluopVWdHsqYu6z+VFgg7t/MGnaAbwuL78F+Bzd0+K+6bBimggh2qHdmCbQV/cc2cRCiDGiM63ooxb3nXYGTRaY2cywmZk9HcIw2vxoJzVQW+mDzOzttRgBxVkOIP9NIUR7nNG2VvQo6NWLyLLivNzMpvPXa8hmlvxM4r/5d3RPi7umw1CmxYppIoRoj3ZjmvQ5EKxsYiHEGNG+VvRZi/tOO3NoPgl81cw+TibKvwpc1sU+tJMaqK30QXle50sAzA4NXyDTwSdrGlI3gbZp5RKQTvcqSz8M9dMJU5ebOH3wwGJ1SWg+pqRtcagvSsrRhSMSZ4IV1o/f0Zq22B06cTspS/McjKQtwaUruuc8OfuIfXuP4wpthxf+1ILH95udzvpEcBWbZmmhfnNwtf7ezUfMVu4s9iH2Cb4f6unnbTVttuw8xmczXffmjrSi24Lv7jfSwCg2s5PJMuZ8gOzhu5LuaXHXdBjKtLiow7sxNxUWQlSfaWJMk2maKUYfDW/ZxAMhfmfHerSnUxv6wJK2uG20Ix6nHLmeDzfp9WmVTjrYm5uOmi1PF5se+0oxzsGXlp49W9m2Z3Hlr4TD3BDqW1Nb/eHQGO+/eL/14/7rTCtGaRCkU0oHTfJp4p8ii/rySjJDfqW7X9/FPnwLODZPC/QAcAHwC2Gdq4F35r6dLwAe68x3E+p/gzQaqBdCiEhzrehXAMI+aPFAdFgqLIRoRr0+NFaMCukwyCYWQgwV7WtFP4NyD4LSQRN3dzNb5e6nkaXt6TruvsPM3glcDywAPubu683sN/L2fwauJfMZvYfs7/Nf6fxIp7KSz89ML5pUlHAhREPOYCX/2pZW9CvoVa+1eFA6/Lmu9F4IUUVOJQvitJRyLa6KDufHkE0shBgi2teKqgeCbeeT3WRmz3f3b/WqE+5+LdmXQLrsn5OyA++Y31GUXk0I0Q6rO9KKPk5F7KkWD0KHlXJYCNGMdlMOQ3V0GGQTCyGGic60Ymzdc3JeBvy6mX0X+BHZvBx39xGLGtWr9Gqd+JPF012WMm3/YtM+YTrUkrBpGtPk+NAW103d7WIa2lZ3xI6mFeTb2SvKzmv0yfxx87ZtIaZJuPaPbZz10bz3uCWFtjs4sVDfmgTG2cqzCm0xpsmtPzqteKA0dVuMafJQqNelWyv5fB1Rdk5PZZJvM8kKYBWt06v17QuiAlqslMNCiPboLOWwdLgzRi3lcCv7uSytcIxhclDJvhTDZHyI1zLYlGkMx3vCqiEFMVuSH1bxd9WNoT4d+5HuPMbxi30chZTD4z1oUhG7NkuZtJI1XARMcgQaVRdC1HMbE9zCSm7JteJQhkQrKqDFsymHp8gS1w3FmRVCDB1pyuEpYM1wKEYFdBhkEwsh2kNaUaNlymF3/667f5dsCM6T14ih9GpCiHY4vaP0ajtY0PFrLlRDi5VyWAjRHp2kHJYOd4psYiFEO3SWcrhfWjwIWg6amNl5ZnY3cC/wH8BGshmTI0bmkzVN6pMlhBCR1R1pxU4WdvyaC9XQ4qIOS4WFEM1YC21rsXS4U2QTCyHaoTOt6JcWD4J2eroSOBP4irsvM7OXAW/qbbd6Qb/8Nzvxd4x55lO/y+BzuSSsGuOWHN+8bfeDY1yIWZ7aEfxAQ3rxcjfS0bnRq0MLH8xCe4l/JsC2UN86W3wk3H8bOSqsuqjpuqs5vXiYm55dPM7thR0H4ueL/p3pvdxJvvr4rJUdd2hjmlRAixXTRAjRHkMa06QCOgyjEdNk9yZlKI9hAkUbekmxaWGIEVgIE/H0YltdvZUtIUaXaE8/MVt8KMQEjPH40jgmMTZfjH+y/dGw4OGkXBbHb1AopkmNljNNgKfc/VFgNzPbzd2/BiHS40iQ+WRNsZIJ1pB5ywohRCSLaTLFHzLBLZRpRe0LotPXHKmAFs/GNDkUqbAQojlpTJNDZ5bUIx2eC7KJhRDt0L5W9FmL+047gyZbzWwf4OvA5Wb29wwmfO88kf+mEKId2o9pAvTzC6ICWqyYJkKI9mg3pglIhztHNrEQoh0604pxHzQ5n2yO0u8AXwT+G3htLzvVG6aDT9b0APsihBhebmlbK/oc9KoCWlzU4emB9kUIMcxME2OaTDdcTzo8F2QTCyHaoX2tqHog2KZBKczMPONH+aJdwGWN1ullB7uHtaj3g1Y+mYkP5qLQdEyox5gmS2eLzz7pvkLTASFpeHqDxlgVT20KfYr92CetxHMYfT8XNimL/tCh720S42RruPD3c3ih/ggHNm37zl3B3zH6c6a+oJtiJx4O9cdDPfXvbPXnXvr5O7n/ooA314psKmJv7+1qabGV1IQQYpZ6fWisGNLhuTAMNnEr0msa7ctnhHqIA5gazYvCZzsgrJrGe9vUyk5XjJPxIbE3t4aYJtF2faikbVOUhI2hnsbuizFMop3bSdzMbtG+VvRDiwdJ2UyTr5nZb5nZEelCM3uamb3czC4D3tLb7nWTU8P0ovHMMS2EaMWZHWlFH6YiVkiLizosFRZCNONUontOc8WQDneKbGIhRDt0phXj6p5zDrAT+JSZbTazO8zsXuBuskjhf+vul/ahj11C6dWEEO0Q3XOaa0Wfgl5VSIuVclgI0R7tphyWDs8F2cRCiHZoXyuqHgi26Rwad98OfAj4kJntTjah7cfuvrVPfesyg0ivFk9vnNZXkiJtSWiK7jjFrK4sPvXumfKJ3FFoW5TmkgWeYHaa2cL9dxbaNixZHnYcjptOa4zpibfHzxems4keUJYeL7S1Sied8PiTxamvm/c4pFD/CXvMlL/73aOLG98UdjYd6vck5R1PhMbonhPTr5W55MRpsgtL2iLpfpcxyV1M8nrgM7RKr9Zrf8xqabFSDgsh2qOTlMPS4U4ZxpTD8Xs6rUdbJ7rjLClWD05cCKL9HN1zEtfkOrto44FhQfS9SPtVXbeE8aDETtwW6vE2SF28YsrhgtEL8ECol7nnDAOdpRwepRglndJOIFjc/Sl3f3B0vxxA6dWEEO0xxQQ3McXvM8FNtNKKnSzs+DVXRl+LlXJYCNEe7aYcBulw58gmFkK0Q2da0U8t7jdtDZpUA6VXE0K0wxlta0XVpyJ2H6UcFkK0R7sph6XDc0E2sRCiHdrXiqpr8RgNmsh/UwjRDu3HNBGdopgmQoj2aDemiZgLsomFEO0grajRck6Mmb0TuNzdf9CH/vSQ1CdrkixbnJNd/OfRm/GjVjFMgq9kms43phheGvZ0ZtEn7gXcPFN+HusKbTGmSZpOdi+KMSV+cNKiQv2he55TPPCSpHxnsYlN+4cF6edXWrbu0CrtXnqPtUgfHWOcJDy+tbjfnfsUR4K3bUl2tjpc27IUwxB8Qct8O6E+pkkn/p7t3nM7gW8BZ5IFZFnOJHczyRuBf6OV/2a/RsmrocXFmCZnA7eR/W/RSyUWQgw/u4B1zOrBCcD1tBfTRDrcKYOwiTsltXei/XxYsbpPsHfSuH9nhk1jTJMt6X5C27aQXnVLDPT3SFJuZa8MIl3suLN7kzLU29MlP4m3h3qMW1JojwFPNoZ6tHPT+2QYUgzXK3EnMU1GaeZIp7TjSHQw8C0zWwN8DLh+dPLQp2Q+WStZw0XAJFPA1Um70q0JMZ7cDHw8fwG8hAm+wUq+kWvFgZTpQx+DXlVAi2djmkyR/SBaA6xK1pASCzGerCPTglV5/T4o6MUa1tNMIaTDnSKbWAjRiHolLmrFEQyJTdx3Wg4lu/sfA8cCHwXeCtxtZn9uZkeXbjh0RJ+sYpsQYlz5qVD/xQ79N/sT9KoaWlzU4XPrWoUQ40p8/s+lk5gm0uHOkE0shGhEvRIPo008CNrNnuNkk5EeIps79CzgKjP7Pz3sW5eJPlnFNiHEuPLNUL98aHPSj74WF3X4mrpWIcS4Ep//a2gvpol0eC7IJhZCNKJeiYfVJu437cQ0eRfwFjKvv48Av+/uT5nZbsDdwB/0tovd4nlMApOcQjYhfDP0JD99WV754HMZ88ynrpItYpqcvqAYOOKFfGOmfBq3FtoWUXS93cqzkrathbbHQ4yM65YeXjzwncnni66dm+Lne3qTsuiM9J6Kfr0xjkxyT0Xf3OjHG9sTV8pdW/YuNG17qFgvpJ2PMUxuD/UY04RHk3KnMUzK/DvnGjfndLJO/iJwOfAGJtk/14qaf3dz+uhLXwEtTnV4LRfm0z97ocRCiNHiJDKXnJoenA38BSva0uJe6rCZLSD7pnsA+BLwq2QW0FNk+Tf/EHiMbNbJD4C3kQXLepe7X5/v4zTgUjJj6Frg3YN16+mXTdwJ8SdJWUyTo4rV00PzOWm5eJr3OXhLob7tocQ4OiDEMNka9vuVYLcXYhM+GtqiTZLGq1B8k/4T7694fcpsyPCobo+xRx5OyhtDWys7d9juhXolnuSIobOJB0E7M00OAH7W3c92939z96cA3H0XMNHT3nWV3ch8sAxYqPz0Qoic1Uzwn0zxG0zwn2Tj6TWtOJUymezzqHoFtDjV4VPZwGzMgkOREgsxzqynqAcbgHa0uA86/O6Z7mQ6fBPw1+5+CPAV4L25Dr8buIDsV8c5wIfyAReADwNvJ3PtOZbiz/oBIJtYCNGI9UEPNjCkNnHfaSemyZ+4+3ebtG1otHz4UX56IUSNnwp6EEPtN8fJgl51+poLVdTiUyjGLJASCzG+zFUPeqnDZraY7C/Xj0Cmw8DLgMvyVS4DVuTl5wFXuPuT7n4v2ZzMM8zsEGBfd/9mPrvkE8k2Q4BsYiFEjbnrQa+02MwWmdlVZnanmW0ws58ys/3M7Mtmdnf+/qxk/QvN7B4zu8vMzu7wBDRldKKvdJVpLiK7GS7K67CsS/tOpxNGd5U4vXCvYjV1dzm+2HTkcUUfh+iCcxY3zJSX3x9+Pz1crKYeHM84/PFC09aQl3bjkUsK9Q3HL2/cX6hPYbs9/bzRPSdOgxu26WmDpOy+iVNDjwz15KIcHJpaueekKdNiOrUtoX57kzLUu+Nsj9c2TTMcpy0+HurRPSdOiSwjlbeyaZf/FfTgRuA5Jeun2EgFsRo2pskmhtfO/W50T4mFEKPFNPV60B5z1uEDzCx1ML3E3S8J6/wdmetj6rt8kLs/CODuD5pZzUfjMLJZKDU25cueopiHtLZ8SOilTVxGK/eI1G4Mtk+0b14c9nTBrAvEa/YvRs86nPsL9fuPnnVDv/6A4u+r7VuC3X5PscrGtF8PhMZo34jeU3YPtQqZENtTe7OVi0167eOPrrIUw/E4w/BbaD560DOb+O+BL7r7G8zsaWQ/oP8Q+Kq7f8DM3gu8F3iPmZ3I7Iy/Q4GvmNlz3X3nfDsxpta+tagLIURrepWT3sw+RuZy84i7n5wv+1Pg14Dv5av9obtf2/WD9xEpsRCixlz1YB46vMXdYzSM2eOb1TT4VjM7q439NeqylywfEqTEQogac9eDXtjEZrYv8NNk2cpw958APzGz84Gz8tUuA24A3gOcTz7jD7jXzO4BzqA+60PHtD+QXylODVOPlI9eiPHlRXN2zwF65b95KY193v/W3Zfmr5EeMIFMedNzLyUWYnyZjx70SIdfBJxnZhuBK4CXm9kngYdzlxvy90fy9TcBafT8xWQRVjdRnJtbWz4kyCYWQtSYnx7MUYsPMLPVyevtyS6fQ/Zn4cfNbMrMPmJmexNm/DEblfkwKEwn69rMvjEdNImp1pReTYjx5ZtBD25pe0vHeuK/6e5fZwzm9q6lmFJUSizE+DJXPeihDl/o7ovdfQnZdO9/d/dfAq4my2RG/v75vHw1cIGZ7WFmR5EFfL0lN+gfN7MzzcyANyfbDAGyiYUQNeauB/PQ4i3ufnrySt0kFwLLgQ+7+zLgR2SuOM3o2cy+MXXPOYlJ7mOyK+nVyvznor9cMZ1vXUyJ1EdzSbHpOO4q1F/AzYX68ruTOCZXh/0+GOqHJNudW4x/8sjxBxbqd3BioV6IaRL6WOdjujH1BY3xXGI9+gBWmeg3Gc9FjFuS1uNgaQgsU3IP1cU0qYtBk5S3hraNoX5nkzLUx0MpuHJDcSygVYrhMt/PVvLVbvyTU5nkxUxyAdmficsgpOluhvc/psk7zezNZOkvf9fd2+vokHIScB3wWbK/XV892O4IIQZIIz1Y1cZ2A9DhDwBXmtnbyHJzvhHA3deb2ZXAHWRfQO9I/Oh/k9mUw9flryGhmzbxfChLORzsohD3L87LfMP+V82Uf4WPF9qODoFJ/ptjZsrPeOa2QtsnJ36tuOPVxSobUxssBjyJtl5qzwxD7IpRpSxGXTznZb/J4rrx/nsiKUd7Mv6n9f2Stlax+YbtXpi7HvRIizcBm9y99sP3KrJBk4fN7JA8rlQ7M/7mzZjONInplJReTYjx5VYmuJEp3skENwJT/Tho2VTEZnwYOJps1uSDwN/0soP9IKYYlRILMb4Msx64+w3uPpGXH3X3V7j7sfn795P13u/uR7v7ce5+XbJ8tbufnLe9M8+iMyTIJhZC1BguPXD3h4D7zey4fNEryAamO5rx142+jOmgidKrCSFqnBn04IyOtp6j/2bZVMSGuPvD7r7T3XcB/9JxR4cQpRwWQtSYjx70KKbJmCCbWAhRY3560CMt/i3gcjNbm3ftz8lm/L3KzO4GXpXXcff1QG3G3xcpzvibF2PqnrM2pFNaiwJfCTGu3BT04BbqfZsa06vsOY2oTUPMq6+jPtnzyLGWYorRhUiJhRhXGulBO/RTh6uJbGIhRI2560GvtNjdp4FGmc5e0WT99wPv73Y/xnTQJPprnQDcRjaathZ4Hu1Pwinzn4und69iNcY0WZSUlxR93J4bYpqcHh0r0zwaXwj7vS/Uj2jexTOOL8ZKuZkXFOrPPm52Z99bfEShrT6myf5JJcbpiLEsok/fqMfAjL6TadySeC5inJJYT85jWRwcKP7WPya0xXUjW5PyltAWXXXTegxZwqOhXpbfvlVMkzLfz078QOOzuJPMPD8d+DGT/BST/Bzwb8DJwDbawTF27upJyuFPkaVSO8DMNgHvA84ys6VkAa02Ar/e9QP3mRjD4GzmrsRCiNFiF7CO2ef9BOB65hjTpAc6PD500ybuhPi9HGNV7NukTPY1nfCcFxVdCH6By2fKr/rSjcWVp8O2p88GYtv58uJ9NH3q0kL99tOfX9z4hqS8Ncani78P0s837HEtBkm8D8ruk7I4OFC8b+L1CXEm60jt0cdDW1lMk2jHjsK1TdV4J5MsY5IJ5hTTpMJaPKaDJpm/1krWcBEwCWQ/oFYl62iUXYjqsgb4ZP6CCWBlnkVnkueQhQ5pA4cdO3oyqv6mBos/2vUDDZg0hsEU2Q8mKbEQ48E6smd9VV6/j6IetO1J3yMdHh9kEwsx3hTVOLOJp3I9OIK2n/+Ka/GY/okX/bXOrWsXQlSZ4iy/oh40mgHYGHdj546FHb9ERoxhICUWYnyIz/e5zC2miXR4vsgmFmK8KT7jc41pUnUtHtNBk5iDejK0T/e3O0KIPlMMpF3Ug5hTsDnZF8SCjl8iYy3Fcy8lFmJ8mA71SYp6sLbN/UiH54tsYiHGm+lCragH7Spx9bV4dIZ3usrzmAQmZ/w1dzHB1EzQm0msZNtWvnZpvcW6e4bmRbPF/RY/Umg6io2F+mF3B3+6/5ot3vsfxaYYjuKEe2fLiw8stu33iu2F+pKTi8c9PAmQ8r0lIabJ4mKVA5LyliWhMfr4hXgvPJyUO417UXac+RCvZ+oP2UmckiXFpn3C/VZ2Hg8IbYvmsW5ka1KOcUrK6ttj5sTo6xl9QdPr18qvt5PrV3YfRHbm0w9rz/zy3H9zLXBU+7txRkrwh41VXASsm9Hi81jFYcxelzIlFkKMNgYFHd4NuJoViW32vPZ2JB2eJ/OxibtJiT0d47kdX6y+kG8U6hP3//ts5S+L6z5RDN3HXi+dLZ9zbNGAvurw4sq3nxximqT2Wl1MkxjbLrWFWtk2oxAHYz6k9nRZbMhG9YUlbfG3RNoeY5jEbSOprRp/h5T9Lom26CjEr7ESm7hNHYbKa/GYzjTZjcw/y/L3pWEqknw3hag2zw/P/ASzetC+LLobO55a0PFL1Chq8VKK00KlxEJUl1MpPu9LZ5Z2psXS4fkim1iI8eZU2cRtMKaDJpE4NbH9qUhCiFHk1i4988aunQs7fonGRHcdKbEQ1aV7z7t0uLvIJhZivOjWM19tLR6dnvaUmG6t/fRK9aTTsFpMwYpnP5l+uGjB1kLTIWwurnt32HbDbHFNaIruOWkPF98bGr9TrB508sPFOonbUHQjWVJS3xamKW4/IawcpzWm9VbuOWk9nvP5TIuLU/f2D/USl5uF4fMe06TcYNO687ooKUeXrjLvsMj2UN8a6mma4VbuOYWUxGUphaH8epW1dUq8tgtL2k5hknuZ5PXAZ5jzM+9Ahaci9puYgvjVg+2OEKKHNHreV81lR9LhLtNNm3g+JDbYotC0pFg9kTuKC66fLd7x78Wmr4Rd/fy1s+WDbiq2Pffwu0qPy8FJ+fboph3dc1LbKNokndg+w+jeAZ2lAk7r0f6PtnaZW02rkAllxN8H8Rqk7lSt3HOealIeFbr0zFdcizXTBKilW5tiJROsoYNEd0KIkWQtE9zEFL/PBDcx52feLfuC6PQlGpKmID4UKbEQVaZrz7t0uMvIJhZivOjSM19xLdagCVCfbk3p1YSoNqd155l3YId1/hINiSmIpcRCVJeuPe/S4S4jm1iI8aJLz3zFtViDJoD8N4UYN7oV00R0E8U0EWJ80PM+rMgmFmK80DPfDoppAtT7cp0A3AaFtHfNxpfKYma0SjsVSOJV7MUThaa9oq/dYzStlyV4BYp7jl16sljdJ+xtfx6dKe++pOjT99QxwX/zoZLjbAppwbbENK9pYI9OUn21SkccfQ3L0p5FP8sQbCRN6RvS33FyqC9t3rbbMT8q1BcdsLVQf9qC2Yvy4yeL5+3xrUVfz11b9p6tFOKOUB+XZFuoP9Sk3GhfpGmGW8UweaKkvZe+n+m1N7JnehkwBZzAJGcwyQoyL/p5+Gx3kulYlJLGOHgAOJzsTmulwkKI0WAXsI7MutpJ9pzPO6YJSIe7ynxs4m6S2GcxntsBsRqMlEcaFoH6KGwFCyXYwHW296JgsxyQ2pAxvsaBoZ7aSvGGjdtGOyo9bvzpNp+bv8wmjsTjxnXL0vvGeC9pTMAYyC/8lojpptNuxI8e7dqCrRp/HT0a6p3E3yv7fTcKKYZTJV5LZhN3KY5RhbVYgyZAzZdrJWvy/NSQhVNdlayjlGtCjC63AVfmL4AzmOAWVnJL/swfypyecafSXxD9Jo1xMAVcnb9qSIWFGG3WkVlWq/L6BLPP+9xjmiAd7iqyiYWoNlGJ7wvP/BHIJq5Hf9wB9b5c59a1CyFGmWWhvqKL/ptzeImGxBgHsU0IMdrE57h7MU3m8BJNkE0sRLWJz/C5sonbQIMmQL0v1zV17UKIUWYq1Fd1x3/TyWZedvoSDYkxDmKbEGK0ic9xV2KaSIe7jGxiIapNfIavkU3cBnLPAeB5TAKTM75dJ5ONJ51HNjn8FJr7+UU/t/SUxlgO4c4ocYnbSTEFU6yzd7Gaugsuub95jyDzTp0hulw+k1LSWCv771/0IX1oSfBDrIuDkbAo1OO6W5PzvS3kbI/1wnl0ShobUOIzG90sY9ySpUn59GLTbmcW45QsPWj2R/txfLvQdiibC/Wn8ZNC/ceJn+j9exxeaLvnoGMK9f/e5+iZ8rZtzy52qiyGSazHtq2hXvAKjn6ireplcX/mQ3xG0zv/ZGAjzMQweQWTHJo888+b2yGdzDFfdIVsKGtdfl3WsLw7HrZCiCHhJOA+srkLmd31WiZZhnR4mIg2cc1q7LYat/r+L/k1tb1Y/UE0KpMweS8N4emeHoKaHLU0qQQ7b2udsRpI422EOCtsiXE8oi2UUhZPA8rtpnieys5rXLcsTklsaxX3L/28S0JbMKhPblJusGqdbZ7ashtD252hvjHJzLI9fp5W5/yHJW1l12cUSJX4GuBsJjlCNnELNNMEyE7DqWTBIk8FNjDBaqb4EyZYjXLUCzHq3M4EtzDFHzLBLcAGis/8PKSwwlMR+0+qxQtn4pscilRYiCqQxi3KQkEuQDo8bDSyidcwxUomWIPUWIhRZ314pmUTt4MGTRqyNPh2LR1gX4QQ82dZd/w1IxX33xwsp3Qn3oEQYmioj1vUhSdbOtxjYowTqbEQo02PnumKa7HccxqSRQ9eSc2362ayU9VOurV0yla8E8J0rticTDd8PKTr2syhxXXjzKnTZotnhSxaT4R8a3ul2X2Xhv0cUaz+mGKK26cl+dieFXw2HloS9pU2xzutbhpjqKfT77aWtEFxmuZ2K7btCC4bZWnrloS2OGUwuODsd9YDM+UXLLi50PZCvhE2XT1TPpr/LrQ9I0zZjNf+v5l1uVkdOvEkexTq9++ZuO/E8xTPcXTB2VTSVudq9v0mZWidgrgfKYd3kj2rtRTDO8IzPU19cNg5UPFI4YNlOlyz9lVYCDEcxMSWO8jysHRVi6XDPSZq8Rr6o8bJRd0amu4pVm99adE2+t6bZv1mnn1/0Rh6wXfCvs6cLd55+pHhMEcX191WYlMuCvvdElzJC7ZRNIqj6060m1IbrBP3nFY2Vplbc3C5L6QJhjqjeZ/k855ZbOKVzevPPu2+QlNMHx1drx667TmzlS+G/Qa3rYJdW3dO4zkvs11bueN0cs4HQb0SyybuHA2aNCT88GYzSrcmxCixFvgMsymGnx/a4zM+Ryr+BTFYitdIKizE6BETWy6H8BdQF7RYOtxjZBMLMdo0UuIU2cTtoD/qGhKn8ivdmhCjxdJQf114prtk4FV8KuJgObVwzaTCQowe9Ykto3tOF7RYOtxjTpVNLMRI0yrFsGzidtCgSUOmlW5NiJFmOtS7lGK4ERX+ghgsxbSXUmEhRo/6xJYxrXiXnmTpcA9RCmIhRpsepRhuRIW1WO45DTmRSXbOMd1aevVb+MCVxJzY9EAxtewdh51YqN921LGF+qk/d/dsJWQ52yvEOCmkGQ5+h98/vhj0I6Zb25ncMjEWxz6Lv1eob9uepLzdhyIHh/rWkno8T2X1+PC1iqWyJCkvLTbt9+IHCvUXLijGKXkZX5spn8UNhbbl924o7mx1Ui66b9adm4NPfqxQ3+tFs76s91O8LyJPbEtSwsW4JBtb1FPfzx3RJ/PhUE/jmEQ/0Fb1+FzMlTJf3FOBB4DXk7npvKw7KYZFH6lPe7mcv1AKYiFGiJjYcg0XsoYN0uKRYlApiBNbIcZkW12sXnvBawr1f9j7t2bKr/uDVYW2Q9lcqKd21fWcXWi7NQazq4v3lhDt3BhDb/vipLJXaOwkFtx84mtEu6ksjfCSYtM+IcbJi8OmE8lRLih+nvP2v7pQfwVfmSkfE+L8xbh+N/OCQv2qU98wU/7OnS3uvUKMkxh/r1U9/Qzx3hyhX/pAz1IMjxmaadIQpVsTYrS5nQluYorfZ4Kb6Go6tRQns0k6fYk2qNdhpSAWYrRIUwxnsUx6oMXS4R4jm1iI0aaHKYZTKq7FGjRpC6VbE2K06FGK4YiTJerp9CXmgFIQCzFq9CTFcEQ63GdkEwsxWvTpma24FmvQpC2iP6f8N4UYbqb688xWPOjVcFHUYamwEMPPWnoUwyRFOtxnZBMLMVr06ZmtuBYrpklbnMQk9zHJucAkWb5rp3F++qealKHODzHG5khjStxedIa8+bAzCvXDub+47ctnA3OdsvTuQpM9Eo6z92zxe4cXnTDXBb+2zSE54BOJ/+OCMDy4aO+thfqTi/eYKT+1Z/CFjLFFtob6tiblRvX0gYt39KJQX1Ks7rN0Ng7LaXvfWmh7CV8v1F/JVwv1l95/y2yl6K5JWBXupjknhPoexerTXvSTmfITwQ/2kUKAGth+T+KPemfY7z0t6ts8qWwKjfEmerhJGer9QrsVw6QVO4HbyHLN72CSFzDJG8himvQoAkbF06sNFyexmVV8lizh5dlkVzv1yNW/AEIMll1kyS3T6BfXw8xz2xMtlg73mU5s4k6INnMaUyLYGTcWg/dt+8izC/W/fuvvzZRveWYxJsYhIabJtiSGxh0U4wduuCukZq2zm5JytD9jjJPtaTyREHwwxPHoLKZJJzZWjGES+rEwsdVDOBfOCfVf8kL1/KOvmCn/CpcW2+79UnHbaCOnvKJYfeKoot17LUn8mq1h2xhzZscTSSXaqq3iyJTFqIwMo49JqsY7mWQZk0zQ06hwFddiDZq0ReYLtpI1XARMMkXxV7Ly0wsxeG4DrsxfWUyyldycP7MH05PntOJfEMPF+pnYCFNkP8TWAKuSNaTEQgyWdWTP5Kq8fh8Unts1rKfrT6p0uM/IJhZi+CmqcWYTT+XP7BGMmk1sZgvIQkA/4O4TZrYf8Gmyv8M3Aj/n7j/I170QeBvZv6nvcvfru9EH/THXFtEXrNgmhBgGlhVqffO5rvBUxOGiqMPn1rUKIQZNfA7PpU9aLB3uI7KJhRh+is9iBWzid5NFsK3xXuCr7n4s2dyl9wKY2YnABWTTac4BPpQPuMwbDZq0RfQFS5nuc1+EEI0puleNsv+mmX3MzB4xs9uTZfuZ2ZfN7O78/Vld/jRDTlGHJ0PrdL+7I4SoYzrUJ1FMk+ohm1iI4We6UBtxm3gx2Rj8R5LF5wOX5eXLgBXJ8ivc/Ul3v5fMqa4Y42KOyD2nLdIc9V9ggilWUjPcrYP9BJ+4HUV/QDYm+7qp2LRmyYsL9T2O+0mh/ij7z5RP3O+OQtuB+0U/vlliPvQHQwyTe0MQkK1JkJCdFAfunkaxT4v23zp7nIXFp2L7PiFH/aKQ0H5rUt5ebKqrp3fxomLTbot/VKgfd9BdhfppyQ/tnw4xTH6G4myuI//9e4U6n03K1xab7ri3WH88KR8Txjv3P6pYZ79idWNyDaK/7R0/KtZZnZRvLzbVxTjZGu4/NiblB0JbjFPycElbv2KY1JNNP6w9m8tz/80e5qDv3VTES4F/BD6RLKuNqn/AzN6b19/Tk6MPJakOr+U8VnEYs9e7EyUWQvQGo6jDuwFXs2Lmue2JFss9p890yyaOcSDiRUxti43FpttDLI5PFqvbN80aUl868/xi4+KSLm0J9XDYOjsqXT92P5i1Bfu0LjZfsInrYo+kdlU8UFk8jRBPkN2L1WNCcxrHZKLYtPgXi8H53srHC/V38KGZ8sF/81hx4yuK1YIJeWZoC/XNHFKo3/XocbOV1cV1665P4QJ2Gn8vrQ9jzJJW2CjZxAeYWXo1L3H3S5L63wF/QDH4z0Hu/iCAuz9oZrUgj4dR/BW9KV82b/oy08Qy/sHM7jGztWa2vMl6l5rZvWY2nb+WJm1n5cvWm9l/9KPfs6Q56l8bpjjJd1OI4eC08GxO0PUc9JEe5aR3969T/43ebFS9Laqlw6fOXOfau5RYiMFzKsXncunM0h5qcY90uFdUS4tlEwsxnJw6SjbxFnc/PXnNDJiY2QTwiLsXp5M3p9HIbfyXeE70a6bJq4Fj89cLgA/n7434fXe/Kl1gZouADwHnuPt9yWjSAMimJdZG7rIRO31JCDF4pvr/bNZy0ndOq1H1RjQbVW+XCulwdnXXMHu9FyIlFmLQNHoue87cdXhQVEiLZRMLMZwM4NnsjRa/CDjPzF5DNn9rXzP7JPCwmR2S28OHMJvmcxNweLL9Yggps+ZIvwZNzgc+4e4O3GRmi2oftM3tfwH4rLvfB+DuMf9pH0lTrV1DllCvWeLLOEcpTv0KH+OeZLphnHIWUpd9c/vLC/XNp8661RwdcqIdSvE078VsCq7oYhPddbYGf5e0HteNLEg+/177lLtsRI+bwrzGVlO9Fs1uvd/BjxaajllQPBdLg4/fS/jPmfLL+Fqh7bAvhT/6Y1rhxCXn1uCOE51b0omk+x8RGkNatx+dXRwB/hpnzZT/k5cU2rZ9pZhmrzAhbTocJ6Ziq5t3+t2k/Ghoi5Me0vqg3HF2kj1zS8k+7AlMcjqTnEd2sXqUUi0yt6mIW9w9JvTrNRXS4ezqXgdKQSzEAGmVYvjVFDNc9YzRcs+pkBZ3YhO3Ik7/SW2LTaEtuK+sPqFY35S4oQR39zr3nNS+jvfR1g7q0eUmkv7iiq470QjeEf8sT913Wv1pnmwbfjuUuuNAwSXnOeevLzS9ueAtDL/O/yvUCy45Hyk0sSm4zSxOf9KG4cJvnXxyob6K1xXqT12RuBzdUNyW7dEFJ7VrO3HHgdFzyalX4+KzOdQ2cVPc/ULgQshm2AG/5+6/ZGZ/BbwF+ED+/vl8k6uBfzWzD5IlbzsWuKUbfenXoMlhwP1JveZf1OgL4v1m9ifkkXDd/UngucDuZnYDmT/T37v7J+KGZvZ24O1Z7Zld7H5KTLUGSnwpxCBYC3wmfwGczgSrWcnq/NlcTF9G1ftnrDcbVW+Xvugw9EeL14NSEAsxYFqlGF7fcKsuM3oxTWQTCyG6TL0aF5/NHqUZTumvFn8AuNLM3kb21fNGAHdfb2ZXAnfkvXmHu3dl/ku//ohr17/oQuB44Plk4TBrQQ4XAqeRRc49G7jIzJ5bt0P3S2r+UMVR2W4SU60p8aUQg2FpqJ/Xv5Rqg+FqstF0KI6qt0tfdBj6o8WnUIydICUWov+0SjGs57AhsomFEF2mXo2rZhO7+w3uPpGXH3X3V7j7sfn795P13u/uR7v7ce5+XbeO37NBEzN7Ry14FdkszZb+Re7+oGc8CXyc2RRBm4AvuvuP3H0L8HUGNnQdU61dU9cuhOgH06F+dX9SqqX0Lr3ap4BvAseZ2aZ8JP0DwKvM7G7gVXm91X4qqsPZ1U2vt5RYiP4Tn7NrKD6XfXkORyDlcHW1WDaxEMNBvRpXxSYeFnrmnuPu/wT8E4CZnQu808yuIPNee6yR72Yy9dzIMkPUkqZ+HvhHM1sIPC3fx9/2qu/lFNNeZv6bQEOfsegP98NQD753W5Ncs6tDWrBI8J387sbjZ8vHH19oe+aSYjCLZ+wxmwB3QYuIPU/ytEJ9Z3LL7Ny5IK5eXHfHbPuT24v72b4t/OuxfY9iPX2I4l26T9H5M41jsmRBMbjIc/l2oX4idzStH3Z38HcsZlerN2meTPoQmkIyPBanl+RnQ+P/KFY/vcfPF+qpP+eG/wpB9r8S9pX67m4MbYTAKwVfTyhPIxzv3cGlFZ7leGA5s8/eq5hkcW9TW0ZqkcK7vVv3NzVpekWH+6moDsMqLgLWzVzvC/MpqX323hVirDmJbF507bk7G/iLXqcYjvRIh7tJdbW4E5u4FfHXU7Q7ygg2yUNJhtGHQhCTGE8kjfvR6ldR7OKOkraybcv205B0IlKLtM7p51kS2o4P9ZDed8+zZm2/00NwxRgT8OC7Q1rh9HIdWmxa/Jxw3PNmi7f9+rGFpvdxcaH+zU8XYzhyaVK+J94jcWAgtWsfD22jHsMkUq/GkxwhLe4i/XLPuRb4DnAP8C/A/6w1mNm1ZlZ7vC43s3VkjlkHAP8bwN03AF8ku+q3AB9x99sZCMW0l7CBCdYwxUomWEOfPHiFELkv9eyzt4Gep7aM1CKFd/oaDBXSYYhavIHZWAqHIiUWoh+ksYUOJVNh6XBLKqTFsomFGA5kE/ea/mSDyyKEv6NJ22uS8ssbrZO3/RXwV93v3Xw5hZWsmvEZm6yAz5gQo8GQPHsjMrWw2jqceeu+jln/3c8NtDdCjAeNnrtVg+jIiOgwVF2Lh+R7WYixY0ievRHS4k5RRsZ5E/055b8pRH8Ygmev4v6bo0SMcSIlFqL3DMVzJx0eIobge1mIsWQInr2Ka3G/Ug5XmLIc9TcBy4BabI/oTxfziSd55x8KydRvCj6MW8OmaQr7kA/9scUHF+sHJPXo2xnr8Q4pyzO/MAR/T/PMxxz0dTnpaU7MMx/qT1swG1xkr+Cj+IxwzmN9jzQwSfysMVDJsaGerH/Uj0Jb8OfkpbPFH76pGK/m8gW/UKj/f7y5UP/mzcmfTVeF/d4Q6oVrH2OY3BPq8f5L45jEe3UYnBSHJAd9SsX9N0eJk4DrgM8CD5BFWXRmPXn1D4EQ3SFV4p1kz9tnycJ+vZoBzDSRDg8RZTZxKzUuu4gx/sQjLdpTeybYOtuDcbc9jbH39GJbnWEY4w2m7S1ijRSIyZJi/6NRnB4n9iHUU9s82s+Lyut77PmTmfJOinELH+HAQv3uY4uxYo59a/JD5I3F/f7whGIfr1rwhpnyP816pwGw5rIXFzf+x9Dn1U+ka4fGB0I9vQ+qFsNENnG/0aDJvGknR/3pA+iXEFVjCHLQR2r+m2LgpLEVpsjyM1+dtA8szY8QFSMq8QSzz91AIlhIh4eIdmxiqbEQ80c2cb/Rn2/zplWO+tP63B8hqsoQ5qCv+FTEUeIUCPdDsU0I0R3i85Q+dwN51qTDQ0Qrm1hqLER3kE3cbzTTZN5kPmQrqfmQTYb2T5FlipsGXgSF6W4dnP4thxXrq0PK3i1JeWPY9uBQPyApt3B9KU3NFtddWDI1sRN3nHjcRWWNsG3RM2bKT+xdnFr5OM8o1Lewf6F+b5KP7WlH/aTQdswzNxXqdgJF0mxrexebtod1b977jJnytbym0PY5VhTqd18fRoZXJeUbQh+CK1YxT/LG0FY2bRE6S+/XD+LUw3jTTIZnby0D+QdrhAS/yvwZK1jDquR+mGUNmdq2M0FcCFGklRKnOjwZvs/6hnR4SGhlE38BeC3tKXHZPP/YFuvpDRFtm31DPbUbo10e3XWia0zZtnHdlLL+NiLdd+x/cDfaltji28KqW0O9aOby2PTsD4b/esELC20/CMb46jCLftFRszuPtve6kO72xrteNVu5NPTpilDf+GhYkLrktLJrU5ecUfcbkU08aDRoMm9ijvpdTDCVGBC3Arfm6z4deP5guinEyBGnHi5ngtQ4P5xJjuxvDvpIxf03R4tUi79Q0OHNaIK4EHOlXokJWrycSSaomg6b2eHAJ8j+etoFXOLuf29m+wGfBpaQ/Tvxc+7+g3ybC4G3kU1Sf5e7X58vP43s5+HTyVIOvzvPolMxWtnEU2SOXDWkxEK0h2ziQaM/2+ZNzFG/tOkUccU2EaITWk09XErfc9BHKp6TfrRItfi1hXtFE8SFmDv1Shxd4SaoqA7vAH7X3U8AzgTeYWYnAu8FvuruxwJfzevkbReQRWA8B/iQmdWmF38YeDtZSPlj8/YK0olNLCUWon1kEw8aDZp0nZjyKWV1vzsjxAgT06VdM/h0apGK+2+OLkUdvqauVQjRLvVKzHBpcY902N0fdPc1eflxYANwGHA+cFm+2mUw45N0PnCFuz/p7veSpaw7w8wOAfZ192/ms0s+kWxTccpsYimxEO0jm3jQyD2n66RTE9eQTQyvpX9aAnydbDRwmvoYJ2WEVFnbQ4yTO5NYHcFHsRDDBIoxQhaFtlbpydL2VumJUzpx14zHif0P8VG28eyZ8n8fH4Ys9yhWf8LTCvVHOGim/G2OK7Ttv1/Rj/IZ+8U0vLM8GmKl3EMxZfTNvGCm/J8Pv6TQtmsyBET5Stj5jUl5U5zNuyHUv5uuHNrKfD2HhZjMchnZv5jXAGczyRGDnXooRoTiFPEL8+msc0mCKcS4UZ/IEu5j9vk5G/gLVlRBiw8ws/TfrEvc/ZJGK5rZErIvpJuBg9z9QcgGVsyslov1MOCmZLNN+bKnKH4h15aPAWU2cbfSEUN5yt4YlyTGOEljj3SacvjpJW3dJD1O/Kwlvw82tkiDHOMNJnfpQ6ufU2h66OBi/d+jbZ6e8i2hbTrUb2xSBoqx+aDezk1TSEe7vMpphWUTDxoNmnSd2tREgIUh/dNuZLNNPpO3K8aJEEWKPpuZv+ZUg/RpQ+IHXRtVF0NGqsOnsoFVhXTE16MYJ0I0oz6RZTGdd/YTZoi0eO46vMXdW/pNm9k+ZIbbb7v7D82a/hBt1OAly8eAMpsYpMRClCGbeJjQn2s9JaZeOy+0K8aJEEWKPpsDT5/WilrQq05foq/EdMSKcSJEc1rFMBm656WHOmxmu5MNmFzu7p/NFz+cu9yQvz+SL98EHJ5svphsasWmvByXjxlKRyxEZ8gmHiY0aNJToi/n1aFdMU6EKFL0yRw6f81GVDjoVVVYC4pxIkSbtIphMpTPSw902LIpJR8FNrj7B5Omq4G35OW3AJ9Pll9gZnuY2VFkAV9vyV15HjezM/N9vjnZZoyINrGUWIhyZBMPE3LP6Skx9doJZP5p7cQ4ifObop9e9MlM8rRvO6jYtC3kcE9nisYYJvOJadIJ8c6L+yqLaRJ9JZP6Y1sOLjStWVI8F/+9uBhrZNEeW2fKzwi+kXvxRKG+IDzZTybxUR4NnfzuA0uKfbwp+YA3EdpCPY6lbU9jq2wMjbH+SFKOMUyGcTi3zHt+kkkWM8kyhtZfs+JTEavCKi4C1jWMcTJJdhc6im8ixpdyz/khj2HSOx1+EfDLwDozm86X/SHwAeBKM3sb2RfWGwHcfb2ZXQnckffoHe5eMxx+k9mUw9flrzGjkU0M3YlxEus7StrKYo90EsOk0fpzpVU8lPS40f4viesR7f/bi/H3uCdsuqhJGVrHMUzjo0Q7/aFQL4T4iZ3oJB5ffPCH0c7tBNnEw4wGTXpK0a8eblOMEyEK1HvPF5+RI5lNnzaEVPwLojqUxzi5muI8wCG924ToGVGJJxjiGCaRHumwu99I43gkAK9oss37gfc3WL4aOLl7vRtFWtnEoBgnYryRTTzM6A+1vqIYJ0IUaZV3fgh9NlMq7r9ZVWKMk9gmxLgR7/uhjmESkQ6PKIpxIkQR2cTDjAZN+kr054wurTcB3yK7677BSDl6CdEWu8im33r+via0T46Gz2YNp9L+m1UlxjhJWUPxDt3V154J0R9aKfHQxzBJkQ6PKNEmngztUmNRdWQTj5IWyz2nr0R/zl1McCsrIZ929W2yB+aT+frbmJ190iqmSerz90hoe0aop/FP9i02Rf/Hh8LM1IVNyq2I63YS02RRaIsxTtL6wbGt2P/HFhVXeGyfpN5pjJZtSTn6b24M9TuT8u2h7aGYeTD6d6Y7ezi0RV/PWO8Vqf9tJzdCbertqry+PE+hVnsGDmeSI4fXd74RFZ6KWFWKMU6+wARTM/fgZjRBXFSfOAl8OQQtXs4kE0iHRe9oZBNPJffgfNT4qSZlKL9Zoj3TKr5gv2KapMeJcVXK7MC4bvg9sD20P5TUH2rVp0h6bsp+o8R6WcySRvURmprQknollk08vGimSV+p+XPW/NGWtpiaeFof+yZEP1gW6nHq4VKKz8iQS1TNf7PTlxgwqRa/VumIxdjRKq1wNoQiHRa9pFObWGosqkYrd5ylyCYeHob87FedVunXbu1zf4ToNVOhfs1oTT2MVNx/czwo6rCSYIpxoFVa4ZG686XDFUEpicW4Ua/EI6vDUHktlnvOQClLvzYJ/Ah4jMap1+J0tnSKXXTHCS44qXtOXVvcNkzd27FXUqYD4q0WpwzGaYAl0wIXldRjW0yh3CplWhnx86bp1baGtphereC+E9OpbQz16IKTulvFKY9x2mKvKEu71+okPkk2WHIamaI+H3gd2XTEVzDJEaM19TCl5r8pRph6HV7OX8wpCaYQw0pZIstrgDVcyBo2jKYWS4crQiubuFvJ4Tv5ldbK0O3Wz6hoy5W5xrRa94clba1SKs+H9Ly2cmtK6/F69MuuHRTlCd5lEw8vGjQZKK3Sr9WSYZKsI8SoMQV8Kn/V/Oa/ld/jhzLUqSxbUfH0auNBvQ6n6YivRzFOxOhTn8iSwn2+hg2MrBZLhyuCbGIxDhTVOLOJp/J7/AhGVoeh8lqsP8yGiph+rdgmxGhSjM0zUunTxBhyimKciMrROoaJ7mwxbMgmFlWkeO9Kh0cHDZoMFdGfs9gmxGhSjM0z0v6ajahw0KvxRDFORPWoVAyTRkiHK4hsYlFFivdupXQYKq3Fcs8ZKlJ/zjVkyS/b9axPfQJbpesqSwsWY5yU+Tt2cvt0kk4trh/in2wNfdyaxmHZi8EQz+OjoZ7GJYkxS1qlER4G/86ynNHx2u4EVgNnALeQTTH8LvBG4EomOYBJljGS/pqRWtArUSGKfvUX5lNoFeNEjBKtYpicDfwFK0bXdz5FOlxR5mMTz5VOb6S53nitbOJOfkmW2eLdjFnSCZ2kea76w1umxpNMslg28YigQZOhIvXnXBh8OUGe9WI0WA1clr8AXsgE32Al38jv5RXMpk8bcSoe9Go8KfrVb2CVYpyIkaNVDJMNwEj7zqdIhyuKbGJRBerVuHgvH4ls4uaY2eHAJ4CDyUagLnH3vzez/YBPA0vIMmr8nLv/IN/mQuBteW/e5e7Xd6Mv+oNsaIm+nPKsF6PCGaH+xur6bPYoJ72ZbTSzdWY2bWare9J30RangGKciJGjVQyTSt23PdJhMUzIJhajSr0ayyamEy3eAfyuu58AnAm8w8xOBN4LfNXdjwW+mtfJ2y4ATgLOAT5kZgu68fE0aDK0KF+9GFVuCfV/q57PZo3eGusvc/el7n56dzstOmEtKMaJGDlaxTCp1H2rQZMxQDaxGFXq1Vg2MW1rsbs/6O5r8vLjZBMlDwPOZ3ZK+2XAirx8PnCFuz/p7vcC91D/b+6ckHvO0FKWr76VP2d0KIsxMlI6zY/eiX/kfBzbSmKalNZjW69y0EPxSY/nLcY4+X6bbcNCPG9ldSMbKDkTuInsnnwx2UDvFcBLmOTAavjORyruvylgFRcB6xrGOJkkmyvqKL6JGDyp5/xOYBlZOsvKxTCJSIfHgPnYxMNIN2/Ysn0NanRwnB/I1hGlJjlCWlzkgDCr+hJ3vySuZGZLyL7abgYOcvcHIRtYMbMD89UOI/sxUmNTvmzeaNBkaGmVrx7kzymGg1uAj+cvgBczwY2s5Mb8Xj2AyvjON2Ju/putviAc+JKZOfD/Gn15iH5RHuPk6vzFzBpCDIboOT9BRWOYNEIxTSqObGIxKrSKYXIE0uI6trSaVW1m+wCfAX7b3X9oZk1XbbDM59SrwDAPw4oC8ucUw8qZoX5Bdf01G+FzeOVfEMkrDoq8yN2XA68m89/86X58FNGaGOMktgkxKOL9V9kYJo2Ymw6LkUU2sRhWxiiGSSN6oMVmtjvZgMnl7v7ZfPHDZnZI3n4Is2lKNwGHJ5svJku9NW80aDIyRH/OydD+BbI77zayqWFC9IqdZDPjPH+/MbR/qrr+mn3C3Tfn748An6NL/phi/sQYJylryBRYSiz6wS6K99ua0F7ZGCZCyCYWQ0MrJZ6UTTwPLJtS8lFgg7t/MGm6GnhLXn4L8Plk+QVmtoeZHQUcS32wxTkh95yRIfpz7mKCKVZCPt1rimwSbo2yKV9p/I3o7xhjc8wnhsmONtsakd6ancQ0mU8Mk1aPQ+zzUyVtMW7Jj5uUh5V4LtL6FPBJZuMvvYQJSO7F/Zmsqu98HzCzvYHd3P3xvPwzwJ8NuFsipxjj5AsFHd6MJoiL/hEngS+HoMXLmWQC6bCoHt20iavMOMcW6Rf1SlzU4cOZ5EjZxHPnRcAvA+vMbDpf9ofAB4ArzextZEFj3gjg7uvN7ErgDrIfZ+9w9644cGqmychQ8+es5fJe2nSKeOWnfokBE90OfzHci0sp3quSmQ45CLjRzG4jGx2/xt2/OOA+iRlSLX6t0hGLgdEqrXA2hCIdFlVENrEYFlq54yxFNvHccfcb3d3c/ZQ8o+RSd7/W3R9191e4+7H5+/eTbd7v7ke7+3Hufl23+qIrN7LEqYnFNiF6x+pQv3yMpx7WQoV3+irZo/t33P3U/HWSu7+/d/0X86Oow0qCKfpJq7TC43MHdl+Hxaghm1gMijFKKdySamux3HNGlnRq4hqyieHNUq+dBKynvVRsnaYgTilzX+mUdNtWLkOpe043b+l4nLLPE/vYzXPRL9LzuBvZ1NbTgFuBF+T1M8hE8cXALwGXAy/OXXI09VCMG/VpMJfzFw2VeJrZ/5lGISGmGA7S5JXTFO+hmMhyDReyhg3SYjGG9MomFgLq0win91B9gvfKphQeczRoMrKk6dcW1qVey+qr8vp9yNNedMYU8Kn8BXB/nkb4spl/cWZTCu9PpdOnleK0js8jqkt9Gsw0HfH1wKHA68gUeJLZSGUwfk+L6JxGaYRr91Pt/qrdb2vYwHhqsXRYyCYWvaRZGuFViU08NR4phUupthZraLUS1KdeUyo2MT9OC/ViGuGxSp9WSrWnIopOOaUuxonSE4v5UJZGuP7+Gte7SjosUmQTi25THrdEOlyj2lqsQZNKEH05oz+dPO1Fp9wa6lcU7qnx9deM1EbVO32JalIf46SZn/04PzWifeJ9Un5/jetdJR0WKbKJRbcpj1siHa5RbS2We04liH71JwV/uhPy9Rr5d36Dopf0MmBBm8cd1OhgPG4n/egkJXF8PMoe7GEcKW31WY3sPlhGNkiyAHh+Xj4duB+4ALgCeAmTHJDfU9OAMVmIzjCu1EbVhYBGWryG9TN+9suZlJe9KFDmKR/jlmT31muZZBn199c4a7F0WKTMxyaeRtGnxpVOIkilcUuydWUTQ9W1WIMmlSD61cf321r4d8Ksp/0Csh/RovrcBlyZv8jzyn86vydqMUxqcUsOYPaeSu+PcfPXjFT7C0J0SpkWLyyNd3Id8rIfR+o95Yv3RYxbMsUCZo15GryPI9JhkdJNmzjdVlSbohpnNvHnZ+LgFO+ZNG6JbOJZqq3FGj4dC1r5d6Ys7VuvxKApDo4V74kL5KPZNtWdiii6SXm8E3nZjyf1nvKKWzI3pMOiXTqxifW8jQ/Fa11+j+i+aE51tViDJmNBK//OlNVk/2d5/v6TUN/Z++6KLrGT8mv5rcLaxXviCvlotkW1g16JblIe72QyrL2GbC5Y7Wmdzsu3kU0iFqPDLppfyzVh3UkUt6RzpMOiEzqxiVMlvo3sB15alxqPDqkSN7qWRTUuv0ekw42pthbLPWcsKPPv/AITTLESEreMNazkM3n9XjIh+Uy+r52M9vSzTh7OUXiQY9yS9JGuXbfatXuACW5Kru0L8umHtWu/nEkmmL1HDpCvfEuqnV5NdJOyeCdrOY9VHMbs87gZpSuuCmVpgzdDQYd3A65mRdP7RFrcCOmw6IRObOLNSldcGcrTBk+yvIVNfIR0uCXV1mLNNBkLav6dNT/ohUn9tUrNVllibJrXh2v7hlCfoPE9ciqSimZUe1RddJMyHT515jmsvStdcXXoJG3wUqDsPpEWN0I6LDpBNvF4Up42uL4um7hzqq3Fuupjj1KzVZepUP9MuLaxrmvbOdVOryb6x1pauWUU1xWjQydpg3Vt54J0WHQL2cTVpTxtsFxwukG1tVjuOWNPJ6nZJsl8An1mXSXJ7AepC058ZHcjGxw5jdkYJbW0waeQ5WJ4PZmLzsuY5OBwrQ/WdMN5Ue1I4aJ/ZBOE1yld8QiitMGDRjosuoVs4tFmrmmD5YLTHaqtxRo0GXs6S80GU6zkavl2Dg1TwKfyV0wbfGYew+SmvH4w5ddadE61/TdFP1G64lFFaYMHjXRYdAvZxKPNXNMGS4u7Q7W1WEOgogXF1Gzy7Rw2TivUitcnxjDR9RFiNFG64mFGaYOFGBdkEw83ShsseocGTUQLiv6d8u0cNm4t1BSzpN9UO+iVGBbK0xVLiQdLvae80gb3F+mw6BeyiYeb4jlXzJJ+U20tlnuOaEHq3zkNGJMF/0CgLU/7bNtZ38Jx9vWMaYIjRnYel5ENiiygGKdkLdkMk1vJzvGZZHFLrmKSAwu+8opZ0muqPRVRDAvl6YovzKcit+NlL6/79mgVpySt7yRT6wlqnvLwF0ob3Eekw6JfyCbuP2VxSsojSE2yONjEilnSW6qtxRo0ES1I/TvTFLb1vp2TUJfPvlgH+HzYh6jnNuDK/BXjlNybxyn5VJO4JSuQr3w/qXbQKzEslPvZb2BVIWbGLorxThT/pHNaxSmJ5/UwZs//BkC+8v1EOiz6hWzi/tNOnJJ4jmvX4EhkE/eTamuxhjXFPDilhb9grBe3Fc1YVqiVxylR3JLBU930amI0OAWa+tkr/snc6DROSVrWOR0E0mExaGQT94ZO4pQobsngqa4Wa9BEzIPW+eyL9eK2ohlThVp5nBLFLRks1fbfFKPBWmjqZ6/4J3Oj0zglaVnntN9Ih8UwIJu4N3QSp0RxSwZLtbXY3H3QfegJZoc6vH3Q3ag4nXh9rwE2k+ZHzyYxt+PrOYpe+LuR9XUp2WdfQBaHZApm/CtrcUmWk7nknA6sBk4Grob/v737j/Wrru84/nythSEwLV2dCG0BCcjmom0FVMIYbMwBEsqymTEJ/KHLwiZ26lBwCgswkCVmxi3o0hTQBTaSYVcrAeuyoS7r6lp620sLShARbsrS8aN2IkM73vvjfO56+r3nfn+e7/d8z/m+Hsk3/Z6f93Peufd1Tj4953N4L9kjOkty23iEgurd+HBEnA4gnRxwax/7uOz/99F0zuJR6P6Z70+wgU0UJ3H7LeuXNr2coXZw6LH/Isyp06e5tMutx70yTbCWiD0C53A3nMOj4Gvi+fUyLknROCWzaXw/sBRfE4+Tybkm9pgmNoBO77PP/7uwp2c94dBnFuv3FP408OX0mX0G877csebHJfkBF7OZm7k7TZ+Vpjd3OU6Jn9GsTrMHvbK6aPec/aH/to5/solDx+aA5oyH0uu4JOS+z9alu3FKimpuo+MctnHga+L59TMuSb/jlPiauDrNzmJ3v9mI9PasZ+uy1n2NvxWHTLUfh+S9HabrcLyTqtm3IlrztI5/0m5sjrqPhzLIuCSty8b9WCebc9jqZtKuiQcZl8TjlNRHs7PYnSY2Ir0963nosvtb9rWd7HGWSP8eyE1PpS2Llg2ybq/bbjukxe3HIfmHDtN+JnN8zfaqN3PQK2ue1vFP2o3N0bqsNYm/ynDSs6yU/mpLe+8f4NidwuPMOWx1M2nXxLP38mV6G5fE45TUR7Oz2GOa2IgM8nT5q1zMRm5m9sJ9VbpVr3gaGMq6vW17Bhez9ZDl2Q2J3TyD6Wcyx1v++c0TAq7rYx9/VIvnN8vgLB437bJ4B+2eNb+EDbwKhRm4h+xxlm6ne9m2rJ/zM8DGrsclcQ6Pt/yYJs7hTpzD42bSronnTvuauCkm55rYnSZWA8EUN7GC7NSxkuuZ4uZ5p4GhrNvbtrcyxZ/mpm8gO+lZ/eVPEMsDPtbHPtbU4gRRBmdxc9zAjfwWFGbgerLxP7qd7mXbsn7OPwI38We9HLKNrXyniXO4E+dwk9Txmrh12tfEzTE518TuqrMaGOQ2xvLW7W3bDb6d0MwapddXG/f7KMwg6/pVwGbWbHW8JvYjNlZ/vtPEamCQ2xjLWnfQbX07YXPke9WXBXykj338SdtedUkXAJ8je1f1uoi4rb+2Vs9Z3CSDvDZyHHLaOdwc+TtNhpPD2b6bkcXO4SbxNbGNk8m5Jh5Jp4mk04C7gFXAJyPiM/OsdzXwYeBk4PUR8VyafzlwbVrtR8AfRsTO9j/TJwgzG4b8CWJpwJo+9nHtvCcISQuAx4HfAGaArcDvRcSj/bY47XfkOZxt5yw2s7LlO03Kz+Fsv83JYuewmQ1HPa+J+7FwRD/nBbIqXtphvX8jGxb6Gy3zvw/8akS8KOlCYC3wjpLbaGbWo9nXq5XqTOCJiHgSQNK9wGpg0BOEc9jMGmgoOQzOYjOzHtTqmrhnI+k0iYi9wF5JrS8Xb11vCkBS6/zNucktwNKy22hm1rs9m+BTS/rY8AhJ+fdSr42Iten78cAzuWUzlHBB7Bw2s2YaSg6Ds9jMrAf1uSbux6juNCnTB4AHixZI+gMO3n/4Cty4a2Stqo8lwHNVN2LMuCbFXJdib579EhEXDGH/RUPKj9vgU/PmMDiLu+C/rWKuSzHXZa5h5zDUPIudw13x39Zcrkkx16XYxFwT16rTRNJ5ZCeIs4uWp16ptWndbXV4fdGouS5zuSbFXJdiLb3hwzADLMtNLwX2DPlndq1TDoOzuBPXpJjrUsx1mWsEOQw1z2LncGeuy1yuSTHXpdgkXRMPbehiSR+UtCN9jithf28F1gGrI+L5wVtoZjaWtgKnSDpJ0uHAZcDGfnbkHDYz65uz2MysWqXl8KCGdqdJRNwO3F7GviQtB9YDV0TE42Xs08xsHEXEgfTWhE1kr1e7MyJ297kv57CZWR+cxWZm1Sozhwc1kpdkSzpW0gzwUeBTkmYkvTYte2C2113SmrTeUmBa0rq0ixuAnwc+n3rpu7kVaG3nVSaS6zKXa1LMdSk29LpExAMRcWpEnBwRt5Sxz4pyGPx7VMQ1Kea6FHNd5hpJTRqUxf4dKua6zOWaFHNditXymrgfihi3Ma3MzMzMzMzMzKo3kjtNzMzMzMzMzMzqxp0mZmZmZmZmZmYFatlpIuk0Sf8u6RVJ17RZ72pJT0gKSUty8y+XNJ0+myW9bTQtHx5l/iod77SkVfOs90VJ38+N4r4it+zcNG+3pG+OrPEjIukCSd9NNbquYHlXNWyCLmpxrqQf5n5PbsgtWyTpPknfkfSYpHeNtvXDIelOSXsl7ZpneWHuSFom6aFUi92S/nh0ra6Oc7iYs7gzZ3HGOTyXc7h3zuK5nMOdOYcPchbP5SwuEBG1+wC/AJwB3AJc02a9lcCJwFPAktz8s4Bj0vcLgW9XfUwl1OQi4EFAwDvnOybgi8DvFMxfBDwKLJ+tcdXHVHJ9FgDfA94EHA7sBH6pnxrW/dNlLc4F7p9n+y8Bv5++Hw4sqvqYSqrLOcAqYNc8ywtzB3gjsCp9/zng8dZ6NvHjHJ73eJ3F7evjLO6+Ds7hucudw13WpGC9icli53DH+jiHe6uFs3ju8onL4lreaRIReyNiK/DTDutNRcRTBfM3R8SLaXIL2cjkdbca+NvIbAEWSXpjD9u/D1gfEU9DVuNhNLJCZwJPRMSTEfET4F6ymuUNWsO66KYWhZSN8H8OcAdARPwkIvYNq6GjFBHfAl5os7wwdyLi2YjYnr7/N/AYcPww2zoOnMPzcha35yzOOIcLOId75ywu5Bxuzzl8kLO4gLN4rlp2mpTsA2Q9qXV3PPBMbnqG+X9Jb0m32n1W0s+meacCx0j6hqSHJV05zMZWoJv69FLDOuv2ON8laaekByW9Jc17E/BfwF2SpiStk3TUkNtbG5JOJPvfvG9X3JS6aUoOg7O4E2dxxjk8JM7hgTQli53D7TmHD3IWD0nTsniiO00knUd2gri26raUQAXzit4n/QngNLJbqhZz8NgXAm8H3gP8JnC9pFOH0M6qdFOfbmtYd90c53bghIh4G/DXwIY0fyHZ7XpfiIiVwEvAnOc/J5Gko4EvAx+OiP1Vt6cuGpbD4CzuxFmccQ4PgXO4fw3LYudwe87hg5zFQ9DELK5Np4mkD+YG4DmuhP29FVgHrI6I5wdv4ejlawLsAZblFi9N8w6RbpuKiHgFuIvstjTIela/FhEvRcRzwLeA2g8GljND5/p0s04TdDzOiNgfET9K3x8ADlM2cNwMMBMRs73G95GdMCaapMPITg73RMT6qtszLM7hYs7injiLM87hkk1KDoOzuIhzuCfO4YOcxSVrahbXptMkIm6PiBXpM9AfraTlwHrgioh4vJwWjl6+JmS9nldmg13rncAPI+LZ1m1mn0eUJOBSYHZU5K8AvyJpoaQjgXeQPYfWFFuBUySdJOlw4DJgY8s6G+mihg3QsRaSjk2/I0g6kywrno+I/wSekfTmtOqvkw2WNrFSne4AHouIv6y6PcPkHC7mLO6JszjjHC7RJOUwOIuLOId74hw+yFlcokZncYzBaLS9foBjyXr39gP70vfXpmUPAMel72vSsgNkvYbr0vx1wIvAjvTZVvUxlVATAbeTjQD9CHB6blm+Jv+Slu8C7gaOzq33MbI/9l1kt1NVflwl1+gislGcvwd8Ms27CriqUw2b9umiFlcDu8lGEd8CnJXbdgWwDZgmuzA5purjKakmfw88Szao1QzZbcr5mhTmDnA22a2c07lMuajq4xlBvZzDxXVxFneukbO4uzo4h53D3dTMWTy3Js7hzjVyDndfC2exsxilAzczMzMzMzMzs5zaPJ5jZmZmZmZmZjZK7jQxMzMzMzMzMyvgThMzMzMzMzMzswLuNDEzMzMzMzMzK+BOEzMzMzMzMzOzAu40scaQ9BpJ35S0oOq2AEi6V9IpVbfDzGxUnMNmZtVzFpuVy50m1iTvB9ZHxP92s7Iyw/wb+ALw8SHu38xs3DiHzcyq5yw2K5E7TWzsSTpD0rSkIyQdJWm3pF8uWPVy4Ctpm6Ml/bOk7ZIekbQ6zT9R0mOSPg9sB5ZJ+nhaZ6ek29J6ayQ9mn7uvWneUZLulLRV0lRunwskfSbtY1rSh1J7/hU4X9LC4VbIzGy4nMNmZtVzFptVQxFRdRvMOpL058ARwGuAmYj4dMvyw4GnI+LYNL0QODIi9ktaAmwBTgFOAJ4EzoqILZIuBK4Hzo+IH0taHBEvSNoDnBQRr0haFBH7JN0KPBoRd0taBPwHsBK4Ejgf+N2IODC7j9SOfwKui4iHh1wiM7Ohcg6bmVXPWWw2eu7ts7q4CdgK/A+wpmD5EmBfblrArZLOAV4FjgfekJb9ICK2pO/nA3dFxI8BZoMdmAbukbQB2JDmvRu4RNI1afoIYHnax99ExIGWfQDsBY4DfIIws7pzDpuZVc9ZbDZi7jSxulgMHA0cRhbML7UsfznNn3U58Hrg7RHxU0lP5ZbntxVQdLvVe4BzgEuA6yW9Ja372xHx3fyKkubbB+lnvtz2yMzM6sE5bGZWPWex2Yh5TBOri7VktwzeA/xF68KIeBFYIGn2JPA6YG86OZxHdgtika8D75d0JICkxcoGwloWEQ+RDVq1iOzktAn4UDohIGllbh9XzT6nKWlxbv+nArv7O2Qzs7HiHDYzq56z2GzE3GliY0/SlcCBiPg74DbgDEm/VrDq14Gz0/d7gNMlbSPrYf9O0b4j4mvARmCbpB3ANcAC4G5JjwBTwGcjYh9wM1mv/rSkXWkaYB3wdJq/E3hfavcbgJcj4tkBDt/MrHLOYTOz6jmLzarhgWCtMVIv90cj4oqq2wIg6SPA/oi4o+q2mJmNgnPYzKx6zmKzcvlOE2uMiJgCHpK0oOq2JPuAL1XdCDOzUXEOm5lVz1lsVi7faWJmZmZmZmZmVsB3mpiZmZmZmZmZFXCniZmZmZmZmZlZAXeamJmZmZmZmZkVcKeJmZmZmZmZmVkBd5qYmZmZmZmZmRX4P/xX6YnObnE+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1296x864 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''add a mask'''\n",
    "mask = al.Mask2D.circular(\n",
    "    shape_native=imaging.shape_native, pixel_scales=imaging.pixel_scales, radius=mask_radius\n",
    ")\n",
    "\n",
    "imaging = imaging.apply_mask(mask=mask)\n",
    "\n",
    "imaging_plotter = aplt.ImagingPlotter(imaging=imaging)\n",
    "\n",
    "imaging_plotter.subplot_imaging()\n",
    "path_prefix = path.join(\"howtolens\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-14 22:36:56,361 - autofit.non_linear.abstract_search - INFO - Creating search\n",
      "2021-12-14 22:36:56,362 - cl_para_pipe_tt5 - INFO - Starting search\n",
      "2021-12-14 22:36:57,499 - autolens.imaging.model.analysis - INFO - PRELOADS - Setting up preloads, may take a few minutes for fits using an inversion.\n",
      "2021-12-14 22:36:58,087 - cl_para_pipe_tt5 - INFO - Saving path info\n",
      "2021-12-14 22:36:58,280 - cl_para_pipe_tt5 - INFO - Not complete. Starting non-linear search.\n",
      "2021-12-14 22:36:58,281 - cl_para_pipe_tt5 - INFO - number_of_cores == 1...\n",
      "2021-12-14 22:36:58,282 - cl_para_pipe_tt5 - INFO - ...not using pool\n",
      "2021-12-14 22:36:58,283 - autofit.non_linear.initializer - INFO - Generating initial samples of model, which are subject to prior limits and other constraints.\n",
      "2021-12-14 22:43:45,940 - cl_para_pipe_tt5 - INFO - No Dynesty samples found, beginning new non-linear search. \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\dynesty.py:373: UserWarning: A note of caution: having `nlive < ndim * (ndim + 1) // 2` may result in unconstrained bounding distributions.\n",
      "  warnings.warn(\"A note of caution: \"\n",
      "3it [00:01,  2.41it/s, bound: 0 | nc: 115 | ncall: 466 | eff(%):  0.644 | loglstar:   -inf <   -inf <    inf | logz:   -inf +/-    nan | dlogz:    inf >  0.209]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "34it [00:06,  6.89it/s, bound: 2 | nc: 10 | ncall: 864 | eff(%):  3.935 | loglstar:   -inf <   -inf <    inf | logz:   -inf +/-    nan | dlogz:    inf >  0.209]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "49it [00:11,  4.38it/s, bound: 3 | nc: 10 | ncall: 1310 | eff(%):  3.740 | loglstar:   -inf <   -inf <    inf | logz:   -inf +/-    nan | dlogz:    inf >  0.209] C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "66it [00:14,  6.60it/s, bound: 4 | nc: 10 | ncall: 1669 | eff(%):  3.954 | loglstar:   -inf <   -inf <    inf | logz:   -inf +/-    nan | dlogz:    inf >  0.209] C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "71it [00:16,  4.18it/s, bound: 5 | nc: 10 | ncall: 1973 | eff(%):  3.599 | loglstar:   -inf <   -inf <    inf | logz:   -inf +/-    nan | dlogz:    inf >  0.209] C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "95it [00:21,  6.35it/s, bound: 6 | nc: 15 | ncall: 2329 | eff(%):  4.079 | loglstar:   -inf <   -inf <    inf | logz:   -inf +/-    nan | dlogz:    inf >  0.209] C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "114it [00:25,  5.57it/s, bound: 7 | nc: 34 | ncall: 2741 | eff(%):  4.159 | loglstar:   -inf <   -inf <    inf | logz:   -inf +/-    nan | dlogz:    inf >  0.209]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "138it [00:33,  6.08it/s, bound: 9 | nc: 10 | ncall: 3862 | eff(%):  3.573 | loglstar:   -inf <   -inf <    inf | logz:   -inf +/-    nan | dlogz:    inf >  0.209] C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "145it [00:36,  3.76it/s, bound: 10 | nc: 10 | ncall: 4257 | eff(%):  3.406 | loglstar:   -inf <   -inf <    inf | logz:   -inf +/-    nan | dlogz:    inf >  0.209]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "178it [00:43,  6.04it/s, bound: 11 | nc: 10 | ncall: 4851 | eff(%):  3.669 | loglstar:   -inf <   -inf <    inf | logz:   -inf +/-    nan | dlogz:    inf >  0.209] C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "179it [00:46,  3.82it/s, +200 | bound: 11 | nc: 1 | ncall: 5582 | eff(%):  6.790 | loglstar:   -inf < -72358.665 <    inf | logz: -72365.554 +/-    nan | dlogz:  1.099 >  0.209]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-14 22:44:32,984 - cl_para_pipe_tt5 - INFO - 5000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "180it [00:00, 1248.93it/s, bound: 12 | nc: 13 | ncall: 5595 | eff(%):  3.217 | loglstar:   -inf <   -inf <    inf | logz:   -inf +/-    nan | dlogz:    inf >  0.209]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "221it [00:10,  8.06it/s, bound: 15 | nc: 10 | ncall: 6779 | eff(%):  3.260 | loglstar:   -inf <   -inf <    inf | logz:   -inf +/-    nan | dlogz:    inf >  0.209]  C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "265it [00:18,  5.32it/s, bound: 17 | nc: 10 | ncall: 7460 | eff(%):  3.552 | loglstar:   -inf <   -inf <    inf | logz:   -inf +/-    nan | dlogz:    inf >  0.209]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "268it [00:20,  2.01it/s, bound: 18 | nc: 18 | ncall: 7898 | eff(%):  3.393 | loglstar:   -inf <   -inf <    inf | logz:   -inf +/-    nan | dlogz:    inf >  0.209] C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "273it [00:22,  3.29it/s, bound: 18 | nc: 10 | ncall: 8080 | eff(%):  3.379 | loglstar:   -inf <   -inf <    inf | logz:   -inf +/-    nan | dlogz:    inf >  0.209] C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "277it [00:28,  1.30it/s, bound: 19 | nc: 10 | ncall: 9110 | eff(%):  3.041 | loglstar:   -inf <   -inf <    inf | logz:   -inf +/-    nan | dlogz:    inf >  0.209]  C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "278it [00:30,  1.13s/it, bound: 19 | nc: 376 | ncall: 9486 | eff(%):  2.931 | loglstar:   -inf <   -inf <    inf | logz:   -inf +/-    nan | dlogz:    inf >  0.209]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "290it [00:34,  4.42it/s, bound: 21 | nc: 10 | ncall: 9921 | eff(%):  2.923 | loglstar:   -inf <   -inf <    inf | logz:   -inf +/-    nan | dlogz:    inf >  0.209] C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "305it [00:37,  4.14it/s, bound: 22 | nc: 10 | ncall: 10243 | eff(%):  2.978 | loglstar:   -inf <   -inf <    inf | logz:   -inf +/-    nan | dlogz:    inf >  0.209] C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "307it [00:39,  1.95it/s, bound: 23 | nc: 10 | ncall: 10555 | eff(%):  2.909 | loglstar:   -inf <   -inf <    inf | logz:   -inf +/-    nan | dlogz:    inf >  0.209] C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "309it [00:40,  7.57it/s, +200 | bound: 23 | nc: 1 | ncall: 10746 | eff(%):  4.737 | loglstar:   -inf < -64519.803 <    inf | logz: -64527.340 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-14 22:45:49,316 - cl_para_pipe_tt5 - INFO - 10000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "310it [00:00, 1057.11it/s, bound: 23 | nc: 10 | ncall: 10756 | eff(%):  2.882 | loglstar:   -inf <   -inf <    inf | logz:   -inf +/-    nan | dlogz:    inf >  0.209]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "342it [00:07, 15.35it/s, bound: 25 | nc: 10 | ncall: 11172 | eff(%):  3.061 | loglstar:   -inf <   -inf <    inf | logz:   -inf +/-    nan | dlogz:    inf >  0.209]  C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "393it [00:17,  6.75it/s, bound: 27 | nc: 10 | ncall: 12027 | eff(%):  3.268 | loglstar:   -inf < -366807.044 <    inf | logz: -366814.996 +/-    nan | dlogz: 342998.518 >  0.209]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "413it [00:21,  5.77it/s, bound: 28 | nc: 10 | ncall: 12378 | eff(%):  3.337 | loglstar:   -inf < -171027.464 <    inf | logz: -171035.515 +/-    nan | dlogz: 115157.351 >  0.209]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "420it [00:25,  3.31it/s, bound: 29 | nc: 10 | ncall: 12884 | eff(%):  3.260 | loglstar:   -inf < -153211.829 <    inf | logz: -153219.915 +/-    nan | dlogz: 98119.412 >  0.209] C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "456it [00:32,  5.45it/s, bound: 31 | nc: 10 | ncall: 13460 | eff(%):  3.388 | loglstar:   -inf < -129875.468 <    inf | logz: -129883.734 +/-    nan | dlogz: 72723.518 >  0.209]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "493it [00:43,  4.52it/s, bound: 33 | nc: 10 | ncall: 14308 | eff(%):  3.446 | loglstar:   -inf < -122442.346 <    inf | logz: -122450.796 +/-    nan | dlogz: 65309.511 >  0.209] C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "495it [00:43,  3.62it/s, bound: 33 | nc: 10 | ncall: 14417 | eff(%):  3.433 | loglstar:   -inf < -122178.734 <    inf | logz: -122187.194 +/-    nan | dlogz: 65125.392 >  0.209]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "510it [00:50,  3.98it/s, bound: 34 | nc: 10 | ncall: 15172 | eff(%):  3.361 | loglstar:   -inf < -119960.891 <    inf | logz: -119969.426 +/-    nan | dlogz: 62870.283 >  0.209] C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "511it [00:53,  9.55it/s, +200 | bound: 34 | nc: 1 | ncall: 15759 | eff(%):  4.512 | loglstar:   -inf < -57279.546 <    inf | logz: -57288.091 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-14 22:47:13,247 - cl_para_pipe_tt5 - INFO - 15000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "558it [00:10,  9.26it/s, bound: 36 | nc: 10 | ncall: 16356 | eff(%):  3.412 | loglstar:   -inf < -111243.819 <    inf | logz: -111252.593 +/-    nan | dlogz: 54261.935 >  0.209] C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "572it [00:13,  6.54it/s, bound: 37 | nc: 225 | ncall: 16711 | eff(%):  3.423 | loglstar:   -inf < -109590.490 <    inf | logz: -109599.334 +/-    nan | dlogz: 52374.605 >  0.209]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "627it [00:31,  5.03it/s, bound: 41 | nc: 10 | ncall: 18500 | eff(%):  3.389 | loglstar:   -inf < -100795.770 <    inf | logz: -100804.889 +/-    nan | dlogz: 65332.291 >  0.209] C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "654it [00:37,  4.38it/s, bound: 43 | nc: 10 | ncall: 18974 | eff(%):  3.447 | loglstar:   -inf < -97780.795 <    inf | logz: -97790.049 +/-    nan | dlogz: 62184.181 >  0.209]  C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "718it [00:50,  4.36it/s, bound: 46 | nc: 10 | ncall: 19966 | eff(%):  3.596 | loglstar:   -inf < -92261.711 <    inf | logz: -92271.283 +/-    nan | dlogz: 56640.299 >  0.209] C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "742it [00:57,  5.17it/s, bound: 48 | nc: 10 | ncall: 20553 | eff(%):  3.610 | loglstar:   -inf < -91048.067 <    inf | logz: -91057.751 +/-    nan | dlogz: 55420.884 >  0.209] C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "743it [00:59, 12.59it/s, +200 | bound: 48 | nc: 1 | ncall: 20827 | eff(%):  4.528 | loglstar:   -inf < -35638.485 <    inf | logz: -35648.187 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-14 22:48:40,577 - cl_para_pipe_tt5 - INFO - 20000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "744it [00:00, 4347.14it/s, bound: 48 | nc: 10 | ncall: 20837 | eff(%):  3.571 | loglstar:   -inf < -90954.210 <    inf | logz: -90963.219 +/-    nan | dlogz: 55337.279 >  0.209]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "841it [00:27,  3.97it/s, bound: 54 | nc: 10 | ncall: 23054 | eff(%):  3.648 | loglstar:   -inf < -86639.639 <    inf | logz: -86649.825 +/-    nan | dlogz: 51042.807 >  0.209]  C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "886it [00:39,  4.58it/s, bound: 56 | nc: 10 | ncall: 23839 | eff(%):  3.717 | loglstar:   -inf < -84124.061 <    inf | logz: -84134.471 +/-    nan | dlogz: 48528.868 >  0.209] C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "888it [00:41,  1.54it/s, bound: 57 | nc: 10 | ncall: 24258 | eff(%):  3.661 | loglstar:   -inf < -83666.800 <    inf | logz: -83677.220 +/-    nan | dlogz: 48271.248 >  0.209] C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "917it [00:48,  4.47it/s, bound: 59 | nc: 10 | ncall: 24929 | eff(%):  3.678 | loglstar:   -inf < -82169.496 <    inf | logz: -82180.061 +/-    nan | dlogz: 46579.865 >  0.209] C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "962it [01:01, 15.67it/s, +200 | bound: 61 | nc: 1 | ncall: 25828 | eff(%):  4.499 | loglstar:   -inf < -35638.485 <    inf | logz: -35649.279 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-14 22:50:09,332 - cl_para_pipe_tt5 - INFO - 25000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "963it [00:00, 2665.30it/s, bound: 61 | nc: 41 | ncall: 25869 | eff(%):  3.723 | loglstar:   -inf < -79625.505 <    inf | logz: -79635.606 +/-    nan | dlogz: 44066.886 >  0.209]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "1039it [00:22,  3.23it/s, bound: 67 | nc: 10 | ncall: 27857 | eff(%):  3.730 | loglstar:   -inf < -75790.922 <    inf | logz: -75801.403 +/-    nan | dlogz: 40159.125 >  0.209]  C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "1054it [00:26,  6.39it/s, bound: 68 | nc: 10 | ncall: 28340 | eff(%):  3.719 | loglstar:   -inf < -74740.005 <    inf | logz: -74751.253 +/-    nan | dlogz: 39317.036 >  0.209] C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "1128it [00:50,  2.19it/s, bound: 73 | nc: 15 | ncall: 30595 | eff(%):  3.687 | loglstar:   -inf < -71489.551 <    inf | logz: -71500.969 +/-    nan | dlogz: 35859.263 >  0.209] C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "1134it [00:52, 21.52it/s, +200 | bound: 73 | nc: 1 | ncall: 30830 | eff(%):  4.327 | loglstar:   -inf < -35638.485 <    inf | logz: -35650.137 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-14 22:51:29,907 - cl_para_pipe_tt5 - INFO - 30000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1135it [00:00, 9734.13it/s, bound: 73 | nc: 10 | ncall: 30840 | eff(%):  3.680 | loglstar:   -inf < -71225.683 <    inf | logz: -71236.642 +/-    nan | dlogz: 35601.417 >  0.209]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "1396it [01:19,  5.11it/s, bound: 88 | nc: 10 | ncall: 35646 | eff(%):  3.916 | loglstar:   -inf < -60663.815 <    inf | logz: -60676.452 +/-    nan | dlogz: 44881.493 >  0.209]  C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "1397it [01:21, 17.12it/s, +200 | bound: 88 | nc: 1 | ncall: 36109 | eff(%):  4.423 | loglstar:   -inf < -15789.997 <    inf | logz: -15802.961 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-14 22:53:19,279 - cl_para_pipe_tt5 - INFO - 35000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1474it [00:26,  2.77it/s, bound: 95 | nc: 43 | ncall: 38059 | eff(%):  3.873 | loglstar:   -inf < -57291.124 <    inf | logz: -57304.467 +/-    nan | dlogz: 41592.274 >  0.209]   C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "1489it [00:35,  3.16it/s, bound: 96 | nc: 10 | ncall: 38807 | eff(%):  3.837 | loglstar:   -inf < -57043.131 <    inf | logz: -57056.218 +/-    nan | dlogz: 41260.757 >  0.209] C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "1542it [01:02,  2.53it/s, bound: 101 | nc: 47 | ncall: 40905 | eff(%):  3.770 | loglstar:   -inf < -54808.324 <    inf | logz: -54821.980 +/-    nan | dlogz: 39028.616 >  0.209] C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "1543it [01:04, 24.00it/s, +200 | bound: 101 | nc: 1 | ncall: 41266 | eff(%):  4.224 | loglstar:   -inf < -15789.997 <    inf | logz: -15803.689 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-14 22:54:52,556 - cl_para_pipe_tt5 - INFO - 40000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1544it [00:00, 4054.35it/s, bound: 102 | nc: 10 | ncall: 41276 | eff(%):  3.741 | loglstar:   -inf < -54796.050 <    inf | logz: -54809.049 +/-    nan | dlogz: 39021.758 >  0.209]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "1582it [00:16, 4054.35it/s, bound: 105 | nc: 10 | ncall: 42521 | eff(%):  3.721 | loglstar:   -inf < -53077.194 <    inf | logz: -53091.076 +/-    nan | dlogz: 37347.972 >  0.209]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "1621it [00:30,  3.31it/s, bound: 107 | nc: 12 | ncall: 43874 | eff(%):  3.695 | loglstar:   -inf < -51613.494 <    inf | logz: -51627.475 +/-    nan | dlogz: 35832.483 >  0.209]  C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "1697it [01:00, 27.94it/s, +200 | bound: 112 | nc: 1 | ncall: 46350 | eff(%):  4.093 | loglstar:   -inf < -15789.997 <    inf | logz: -15804.458 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-14 22:56:21,968 - cl_para_pipe_tt5 - INFO - 45000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1954it [01:22,  4.04it/s, bound: 125 | nc: 10 | ncall: 50610 | eff(%):  3.861 | loglstar:   -inf < -39663.119 <    inf | logz: -39678.856 +/-    nan | dlogz: 27200.907 >  0.209]   C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "1983it [01:34, 20.94it/s, +200 | bound: 127 | nc: 1 | ncall: 51355 | eff(%):  4.251 | loglstar:   -inf < -12481.768 <    inf | logz: -12497.655 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-14 22:58:27,475 - cl_para_pipe_tt5 - INFO - 50000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2248it [01:51, 20.24it/s, +200 | bound: 142 | nc: 1 | ncall: 56573 | eff(%):  4.327 | loglstar:   -inf < -9335.571 <    inf | logz: -9352.779 +/-    nan | dlogz:  1.099 >  0.209]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-14 23:00:48,134 - cl_para_pipe_tt5 - INFO - 55000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2575it [02:17, 18.68it/s, +200 | bound: 158 | nc: 1 | ncall: 61574 | eff(%):  4.507 | loglstar:   -inf < -5079.156 <    inf | logz: -5097.995 +/-    nan | dlogz:  1.099 >  0.209] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-14 23:03:35,480 - cl_para_pipe_tt5 - INFO - 60000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2576it [00:00, 8967.42it/s, bound: 158 | nc: 10 | ncall: 61584 | eff(%):  4.183 | loglstar:   -inf < -23989.667 <    inf | logz: -24007.690 +/-    nan | dlogz: 18917.848 >  0.209]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "2962it [02:33, 19.27it/s, +200 | bound: 174 | nc: 1 | ncall: 66582 | eff(%):  4.749 | loglstar:   -inf < -1919.097 <    inf | logz: -1939.867 +/-    nan | dlogz:  1.099 >  0.209] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-14 23:06:38,482 - cl_para_pipe_tt5 - INFO - 65000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "3356it [02:39, 20.98it/s, +200 | bound: 190 | nc: 1 | ncall: 71585 | eff(%):  4.968 | loglstar:   -inf < 4129.148 <    inf | logz: 4106.414 +/-    nan | dlogz:  1.099 >  0.209]    \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-14 23:09:49,446 - cl_para_pipe_tt5 - INFO - 70000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "3696it [02:39, 23.10it/s, +200 | bound: 205 | nc: 1 | ncall: 76822 | eff(%):  5.071 | loglstar:   -inf < 9148.799 <    inf | logz: 9124.369 +/-    nan | dlogz:  1.099 >  0.209]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-14 23:13:00,187 - cl_para_pipe_tt5 - INFO - 75000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "3945it [01:48,  2.51it/s, bound: 216 | nc: 10 | ncall: 80225 | eff(%):  4.917 | loglstar:   -inf < 409.218 <    inf | logz: 384.284 +/-    nan | dlogz: 8746.185 >  0.209]       C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "4013it [02:38, 25.40it/s, +200 | bound: 219 | nc: 1 | ncall: 81828 | eff(%):  5.149 | loglstar:   -inf < 9148.799 <    inf | logz: 9122.788 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-14 23:16:09,204 - cl_para_pipe_tt5 - INFO - 80000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "4410it [02:44, 26.76it/s, +200 | bound: 235 | nc: 1 | ncall: 86837 | eff(%):  5.309 | loglstar:   -inf < 15174.059 <    inf | logz: 15146.068 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-14 23:19:27,054 - cl_para_pipe_tt5 - INFO - 85000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "4807it [02:43, 29.44it/s, +200 | bound: 252 | nc: 1 | ncall: 91892 | eff(%):  5.449 | loglstar:   -inf < 19047.762 <    inf | logz: 19017.791 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-14 23:22:43,171 - cl_para_pipe_tt5 - INFO - 90000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "5169it [02:45, 31.14it/s, +200 | bound: 268 | nc: 1 | ncall: 96894 | eff(%):  5.541 | loglstar:   -inf < 19136.077 <    inf | logz: 19104.300 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-14 23:26:03,630 - cl_para_pipe_tt5 - INFO - 95000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "5531it [02:47, 33.06it/s, +200 | bound: 284 | nc: 1 | ncall: 101901 | eff(%):  5.624 | loglstar:   -inf < 20300.089 <    inf | logz: 20266.506 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-14 23:29:24,918 - cl_para_pipe_tt5 - INFO - 100000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "5939it [02:46, 35.61it/s, +200 | bound: 301 | nc: 1 | ncall: 106902 | eff(%):  5.743 | loglstar:   -inf < 21967.337 <    inf | logz: 21931.719 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-14 23:32:47,998 - cl_para_pipe_tt5 - INFO - 105000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "6315it [02:44, 38.30it/s, +200 | bound: 317 | nc: 1 | ncall: 111908 | eff(%):  5.822 | loglstar:   -inf < 23624.208 <    inf | logz: 23586.715 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-14 23:36:08,025 - cl_para_pipe_tt5 - INFO - 110000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "6616it [02:42, 40.84it/s, +200 | bound: 331 | nc: 1 | ncall: 116918 | eff(%):  5.830 | loglstar:   -inf < 24621.191 <    inf | logz: 24582.197 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-14 23:39:25,980 - cl_para_pipe_tt5 - INFO - 115000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "6939it [02:45, 41.91it/s, +200 | bound: 346 | nc: 1 | ncall: 121928 | eff(%):  5.855 | loglstar:   -inf < 25840.514 <    inf | logz: 25799.909 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-14 23:42:49,962 - cl_para_pipe_tt5 - INFO - 120000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "7223it [02:13,  2.81it/s, bound: 359 | nc: 10 | ncall: 125943 | eff(%):  5.735 | loglstar:   -inf < 23692.456 <    inf | logz: 23651.036 +/-    nan | dlogz: 2466.581 >  0.209]   C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "7224it [03:03, 39.47it/s, +200 | bound: 359 | nc: 1 | ncall: 127473 | eff(%):  5.824 | loglstar:   -inf < 26152.231 <    inf | logz: 26110.204 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-14 23:46:30,963 - cl_para_pipe_tt5 - INFO - 125000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "7260it [00:34,  1.31it/s, bound: 362 | nc: 10 | ncall: 128519 | eff(%):  5.649 | loglstar:   -inf < 23796.081 <    inf | logz: 23754.100 +/-    nan | dlogz: 2363.943 >  0.209]   C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "7353it [02:42, 45.23it/s, +200 | bound: 367 | nc: 1 | ncall: 132481 | eff(%):  5.701 | loglstar:   -inf < 26152.231 <    inf | logz: 26109.561 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-14 23:49:50,862 - cl_para_pipe_tt5 - INFO - 130000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "7599it [02:28,  2.29it/s, bound: 380 | nc: 15 | ncall: 137028 | eff(%):  5.546 | loglstar:   -inf < 24880.031 <    inf | logz: 24836.370 +/-    nan | dlogz: 3378.802 >  0.209]   C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "7600it [02:53, 43.76it/s, +200 | bound: 380 | nc: 1 | ncall: 137815 | eff(%):  5.660 | loglstar:   -inf < 28250.797 <    inf | logz: 28206.895 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-14 23:53:21,706 - cl_para_pipe_tt5 - INFO - 135000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "7899it [02:46, 47.46it/s, +200 | bound: 395 | nc: 1 | ncall: 142822 | eff(%):  5.671 | loglstar:   -inf < 28374.409 <    inf | logz: 28329.016 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-14 23:56:45,717 - cl_para_pipe_tt5 - INFO - 140000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "8190it [02:37,  2.38it/s, bound: 411 | nc: 17 | ncall: 147638 | eff(%):  5.547 | loglstar:   -inf < 26549.505 <    inf | logz: 26502.709 +/-    nan | dlogz: 2277.307 >  0.209]  C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "8191it [03:08, 43.49it/s, +200 | bound: 412 | nc: 1 | ncall: 148598 | eff(%):  5.647 | loglstar:   -inf < 28817.008 <    inf | logz: 28770.159 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 00:00:35,612 - cl_para_pipe_tt5 - INFO - 145000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "8508it [02:29,  2.31it/s, bound: 427 | nc: 10 | ncall: 153055 | eff(%):  5.559 | loglstar:   -inf < 27279.767 <    inf | logz: 27231.622 +/-    nan | dlogz: 1893.247 >  0.209]   C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "8511it [02:47, 50.71it/s, +200 | bound: 428 | nc: 1 | ncall: 153623 | eff(%):  5.670 | loglstar:   -inf < 29165.414 <    inf | logz: 29116.968 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 00:04:02,030 - cl_para_pipe_tt5 - INFO - 150000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "8619it [01:03,  2.29it/s, bound: 433 | nc: 10 | ncall: 155522 | eff(%):  5.542 | loglstar:   -inf < 27562.684 <    inf | logz: 27513.997 +/-    nan | dlogz: 1610.307 >  0.209]   C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "8722it [02:31,  1.88it/s, bound: 438 | nc: 26 | ncall: 158152 | eff(%):  5.515 | loglstar:   -inf < 27767.063 <    inf | logz: 27718.066 +/-    nan | dlogz: 1881.173 >  0.209]  C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "8723it [02:56, 49.38it/s, +200 | bound: 438 | nc: 1 | ncall: 158886 | eff(%):  5.616 | loglstar:   -inf < 29641.218 <    inf | logz: 29591.715 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 00:07:41,983 - cl_para_pipe_tt5 - INFO - 155000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "8803it [00:36,  2.53it/s, bound: 442 | nc: 10 | ncall: 159949 | eff(%):  5.504 | loglstar:   -inf < 27949.590 <    inf | logz: 27900.567 +/-    nan | dlogz: 1697.920 >  0.209]   C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "8868it [02:52, 51.48it/s, +200 | bound: 446 | nc: 1 | ncall: 163977 | eff(%):  5.530 | loglstar:   -inf < 29641.218 <    inf | logz: 29590.992 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 00:11:13,479 - cl_para_pipe_tt5 - INFO - 160000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "8898it [00:13,  4.97it/s, bound: 447 | nc: 10 | ncall: 164359 | eff(%):  5.414 | loglstar:   -inf < 28120.699 <    inf | logz: 28070.839 +/-    nan | dlogz: 1527.171 >  0.209]   C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "8899it [02:51, 52.03it/s, +200 | bound: 447 | nc: 1 | ncall: 168983 | eff(%):  5.385 | loglstar:   -inf < 29641.218 <    inf | logz: 29590.837 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 00:14:46,772 - cl_para_pipe_tt5 - INFO - 165000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "9255it [02:51, 53.81it/s, +200 | bound: 463 | nc: 1 | ncall: 173984 | eff(%):  5.434 | loglstar:   -inf < 29641.218 <    inf | logz: 29589.065 +/-    nan | dlogz:  1.097 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 00:18:18,685 - cl_para_pipe_tt5 - INFO - 170000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "9633it [02:54, 55.26it/s, +200 | bound: 480 | nc: 1 | ncall: 179003 | eff(%):  5.493 | loglstar:   -inf < 30340.439 <    inf | logz: 30286.398 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 00:21:53,317 - cl_para_pipe_tt5 - INFO - 175000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "9982it [02:54, 57.36it/s, +200 | bound: 496 | nc: 1 | ncall: 184011 | eff(%):  5.533 | loglstar:   -inf < 30573.317 <    inf | logz: 30517.535 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 00:25:27,980 - cl_para_pipe_tt5 - INFO - 180000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10391it [02:55, 59.18it/s, +200 | bound: 515 | nc: 1 | ncall: 189016 | eff(%):  5.603 | loglstar:   -inf < 30790.903 <    inf | logz: 30733.081 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 00:29:04,895 - cl_para_pipe_tt5 - INFO - 185000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10639it [02:05,  2.60it/s, bound: 526 | nc: 10 | ncall: 192633 | eff(%):  5.523 | loglstar:   -inf < 30218.494 <    inf | logz: 30160.647 +/-    nan | dlogz: 823.155 >  0.209]   C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "10661it [02:31,  2.80it/s, bound: 527 | nc: 10 | ncall: 193376 | eff(%):  5.513 | loglstar:   -inf < 30243.603 <    inf | logz: 30184.983 +/-    nan | dlogz: 799.158 >  0.209] C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "10662it [02:59, 59.27it/s, +200 | bound: 527 | nc: 1 | ncall: 194207 | eff(%):  5.593 | loglstar:   -inf < 31036.160 <    inf | logz: 30976.986 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 00:32:46,960 - cl_para_pipe_tt5 - INFO - 190000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "11000it [02:48, 65.18it/s, +200 | bound: 543 | nc: 1 | ncall: 199211 | eff(%):  5.622 | loglstar:   -inf < 31041.714 <    inf | logz: 30980.863 +/-    nan | dlogz:  1.093 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 00:36:21,552 - cl_para_pipe_tt5 - INFO - 195000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "11038it [00:20,  2.57it/s, bound: 545 | nc: 10 | ncall: 199804 | eff(%):  5.524 | loglstar:   -inf < 30553.682 <    inf | logz: 30493.437 +/-    nan | dlogz: 563.124 >  0.209]   C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "11069it [01:50,  1.54it/s, bound: 547 | nc: 42 | ncall: 202372 | eff(%):  5.470 | loglstar:   -inf < 30569.297 <    inf | logz: 30510.123 +/-    nan | dlogz: 545.632 >  0.209]  C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "11153it [02:53, 64.43it/s, +200 | bound: 551 | nc: 1 | ncall: 204219 | eff(%):  5.559 | loglstar:   -inf < 31305.121 <    inf | logz: 31243.498 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 00:39:58,919 - cl_para_pipe_tt5 - INFO - 200000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "11183it [00:17, 31303.04it/s, bound: 552 | nc: 10 | ncall: 204557 | eff(%):  5.467 | loglstar:   -inf < 30647.728 <    inf | logz: 30587.084 +/-    nan | dlogz: 663.152 >  0.209]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "11205it [00:42,  2.79it/s, bound: 553 | nc: 10 | ncall: 205416 | eff(%):  5.455 | loglstar:   -inf < 30664.026 <    inf | logz: 30603.717 +/-    nan | dlogz: 645.990 >  0.209]   C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "11206it [02:19, 29.44s/it, bound: 553 | nc: 2854 | ncall: 208270 | eff(%):  5.381 | loglstar:   -inf < 30665.083 <    inf | logz: 30604.307 +/-    nan | dlogz: 645.518 >  0.209]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "11207it [03:13, 57.94it/s, +200 | bound: 554 | nc: 1 | ncall: 209783 | eff(%):  5.438 | loglstar:   -inf < 31305.121 <    inf | logz: 31243.229 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 00:43:56,649 - cl_para_pipe_tt5 - INFO - 205000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "11312it [00:41,  2.69it/s, bound: 558 | nc: 12 | ncall: 210981 | eff(%):  5.362 | loglstar:   -inf < 30737.277 <    inf | logz: 30676.599 +/-    nan | dlogz: 725.320 >  0.209]   C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "11448it [02:34,  2.66it/s, bound: 565 | nc: 10 | ncall: 214239 | eff(%):  5.344 | loglstar:   -inf < 30823.658 <    inf | logz: 30762.261 +/-    nan | dlogz: 638.963 >  0.209]  C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "11449it [03:28, 54.94it/s, +200 | bound: 565 | nc: 1 | ncall: 215819 | eff(%):  5.398 | loglstar:   -inf < 31457.899 <    inf | logz: 31394.800 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 00:48:09,232 - cl_para_pipe_tt5 - INFO - 210000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "11577it [00:55,  1.29it/s, bound: 571 | nc: 49 | ncall: 217441 | eff(%):  5.324 | loglstar:   -inf < 30897.251 <    inf | logz: 30835.032 +/-    nan | dlogz: 629.154 >  0.209]   C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "11685it [02:29,  2.12it/s, bound: 577 | nc: 10 | ncall: 220240 | eff(%):  5.306 | loglstar:   -inf < 30964.709 <    inf | logz: 30902.647 +/-    nan | dlogz: 628.824 >  0.209]  C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "11686it [04:17, 45.30it/s, +200 | bound: 577 | nc: 1 | ncall: 223475 | eff(%):  5.319 | loglstar:   -inf < 31589.498 <    inf | logz: 31525.217 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 00:53:13,550 - cl_para_pipe_tt5 - INFO - 215000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "11848it [01:46,  2.06it/s, bound: 586 | nc: 12 | ncall: 226469 | eff(%):  5.232 | loglstar:   -inf < 31051.807 <    inf | logz: 30988.131 +/-    nan | dlogz: 696.495 >  0.209]   C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "11849it [03:17, 60.02it/s, +200 | bound: 586 | nc: 1 | ncall: 229022 | eff(%):  5.261 | loglstar:   -inf < 31743.209 <    inf | logz: 31678.116 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 00:57:20,919 - cl_para_pipe_tt5 - INFO - 220000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "11936it [00:51,  2.26it/s, bound: 591 | nc: 10 | ncall: 230576 | eff(%):  5.177 | loglstar:   -inf < 31109.258 <    inf | logz: 31045.933 +/-    nan | dlogz: 637.982 >  0.209]   C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "11977it [02:23,  2.95it/s, bound: 593 | nc: 10 | ncall: 233582 | eff(%):  5.128 | loglstar:   -inf < 31131.496 <    inf | logz: 31067.811 +/-    nan | dlogz: 615.944 >  0.209]  C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "11978it [03:48, 52.46it/s, +200 | bound: 593 | nc: 1 | ncall: 236217 | eff(%):  5.155 | loglstar:   -inf < 31743.209 <    inf | logz: 31677.472 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 01:01:53,802 - cl_para_pipe_tt5 - INFO - 225000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "12121it [01:17,  2.02it/s, bound: 601 | nc: 10 | ncall: 238429 | eff(%):  5.084 | loglstar:   -inf < 31224.675 <    inf | logz: 31160.013 +/-    nan | dlogz: 577.693 >  0.209]   C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "12251it [02:53, 70.75it/s, +200 | bound: 607 | nc: 1 | ncall: 241219 | eff(%):  5.162 | loglstar:   -inf < 31908.647 <    inf | logz: 31841.548 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 01:05:31,836 - cl_para_pipe_tt5 - INFO - 230000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "12264it [00:14, 33628.87it/s, bound: 608 | nc: 10 | ncall: 241350 | eff(%):  5.081 | loglstar:   -inf < 31317.210 <    inf | logz: 31251.673 +/-    nan | dlogz: 596.217 >  0.209]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "12277it [02:11,  2.51it/s, bound: 609 | nc: 10 | ncall: 245057 | eff(%):  5.010 | loglstar:   -inf < 31324.280 <    inf | logz: 31258.762 +/-    nan | dlogz: 589.066 >  0.209]   C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "12285it [02:51, 71.82it/s, +200 | bound: 611 | nc: 1 | ncall: 246220 | eff(%):  5.071 | loglstar:   -inf < 31908.647 <    inf | logz: 31841.378 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 01:09:10,967 - cl_para_pipe_tt5 - INFO - 235000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "12309it [00:25,  2.18it/s, bound: 612 | nc: 18 | ncall: 246927 | eff(%):  4.985 | loglstar:   -inf < 31342.819 <    inf | logz: 31276.719 +/-    nan | dlogz: 571.081 >  0.209]   C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "12318it [01:22,  1.46s/it, bound: 613 | nc: 10 | ncall: 248591 | eff(%):  4.955 | loglstar:   -inf < 31347.596 <    inf | logz: 31281.871 +/-    nan | dlogz: 565.744 >  0.209]  C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "12328it [02:07,  4.26s/it, bound: 614 | nc: 337 | ncall: 249879 | eff(%):  4.934 | loglstar:   -inf < 31355.432 <    inf | logz: 31289.114 +/-    nan | dlogz: 558.867 >  0.209]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "12343it [02:44,  1.83it/s, bound: 617 | nc: 10 | ncall: 250918 | eff(%):  4.919 | loglstar:   -inf < 31364.272 <    inf | logz: 31297.881 +/-    nan | dlogz: 549.855 >  0.209] C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "12344it [03:06, 66.22it/s, +200 | bound: 617 | nc: 1 | ncall: 251561 | eff(%):  4.986 | loglstar:   -inf < 31908.647 <    inf | logz: 31841.084 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 01:13:05,458 - cl_para_pipe_tt5 - INFO - 240000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "12360it [00:25,  1.59s/it, bound: 620 | nc: 10 | ncall: 252285 | eff(%):  4.899 | loglstar:   -inf < 31373.857 <    inf | logz: 31308.003 +/-    nan | dlogz: 539.315 >  0.209]   C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "12361it [00:47,  7.87s/it, bound: 620 | nc: 638 | ncall: 252923 | eff(%):  4.887 | loglstar:   -inf < 31373.986 <    inf | logz: 31308.309 +/-    nan | dlogz: 538.998 >  0.209]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "12368it [01:18,  2.67s/it, bound: 622 | nc: 87 | ncall: 253817 | eff(%):  4.873 | loglstar:   -inf < 31378.946 <    inf | logz: 31312.963 +/-    nan | dlogz: 534.423 >  0.209] C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "12386it [01:43,  2.44it/s, bound: 623 | nc: 10 | ncall: 254553 | eff(%):  4.866 | loglstar:   -inf < 31394.954 <    inf | logz: 31328.427 +/-    nan | dlogz: 519.086 >  0.209] C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "12389it [02:51, 72.24it/s, +200 | bound: 624 | nc: 1 | ncall: 256568 | eff(%):  4.907 | loglstar:   -inf < 31908.647 <    inf | logz: 31840.859 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 01:16:45,121 - cl_para_pipe_tt5 - INFO - 245000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "12408it [00:11, 27692.91it/s, bound: 624 | nc: 10 | ncall: 256768 | eff(%):  4.832 | loglstar:   -inf < 31406.264 <    inf | logz: 31339.967 +/-    nan | dlogz: 507.231 >  0.209]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "12428it [00:55,  2.07it/s, bound: 626 | nc: 21 | ncall: 258171 | eff(%):  4.814 | loglstar:   -inf < 31418.280 <    inf | logz: 31351.757 +/-    nan | dlogz: 495.441 >  0.209]   C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "12458it [02:08,  1.28it/s, bound: 628 | nc: 10 | ncall: 260301 | eff(%):  4.786 | loglstar:   -inf < 31433.943 <    inf | logz: 31366.821 +/-    nan | dlogz: 480.542 >  0.209]  C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "12460it [02:34,  5.96s/it, bound: 629 | nc: 10 | ncall: 261072 | eff(%):  4.773 | loglstar:   -inf < 31436.133 <    inf | logz: 31368.790 +/-    nan | dlogz: 478.712 >  0.209] C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "12461it [02:52, 72.26it/s, +200 | bound: 629 | nc: 1 | ncall: 261588 | eff(%):  4.840 | loglstar:   -inf < 31908.647 <    inf | logz: 31840.500 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 01:20:25,693 - cl_para_pipe_tt5 - INFO - 250000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "12471it [00:16, 33113.51it/s, bound: 630 | nc: 10 | ncall: 261883 | eff(%):  4.762 | loglstar:   -inf < 31439.615 <    inf | logz: 31373.467 +/-    nan | dlogz: 473.271 >  0.209]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "12492it [01:13,  2.11it/s, bound: 632 | nc: 10 | ncall: 263746 | eff(%):  4.736 | loglstar:   -inf < 31451.812 <    inf | logz: 31384.506 +/-    nan | dlogz: 462.559 >  0.209]   C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "12493it [03:05, 67.40it/s, +200 | bound: 632 | nc: 1 | ncall: 267067 | eff(%):  4.753 | loglstar:   -inf < 31908.647 <    inf | logz: 31840.341 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 01:24:24,054 - cl_para_pipe_tt5 - INFO - 255000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "12519it [00:12, 28369.69it/s, bound: 633 | nc: 10 | ncall: 267342 | eff(%):  4.683 | loglstar:   -inf < 31466.765 <    inf | logz: 31400.114 +/-    nan | dlogz: 446.466 >  0.209]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "12521it [00:33,  2.60it/s, bound: 634 | nc: 10 | ncall: 268005 | eff(%):  4.672 | loglstar:   -inf < 31467.320 <    inf | logz: 31400.734 +/-    nan | dlogz: 445.775 >  0.209]   C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "12541it [01:01,  2.65it/s, bound: 635 | nc: 10 | ncall: 268810 | eff(%):  4.665 | loglstar:   -inf < 31477.186 <    inf | logz: 31410.663 +/-    nan | dlogz: 598.394 >  0.209] C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "12594it [02:21,  2.32it/s, bound: 638 | nc: 10 | ncall: 271049 | eff(%):  4.646 | loglstar:   -inf < 31502.554 <    inf | logz: 31435.208 +/-    nan | dlogz: 573.662 >  0.209]  C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "12595it [04:04, 51.52it/s, +200 | bound: 639 | nc: 1 | ncall: 274065 | eff(%):  4.669 | loglstar:   -inf < 32071.302 <    inf | logz: 32002.487 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 01:29:17,239 - cl_para_pipe_tt5 - INFO - 260000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "12603it [00:10, 30251.37it/s, bound: 640 | nc: 10 | ncall: 274149 | eff(%):  4.597 | loglstar:   -inf < 31508.285 <    inf | logz: 31440.998 +/-    nan | dlogz: 567.906 >  0.209]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "12636it [02:40,  2.55it/s, bound: 643 | nc: 10 | ncall: 278676 | eff(%):  4.534 | loglstar:   -inf < 31524.354 <    inf | logz: 31456.685 +/-    nan | dlogz: 552.079 >  0.209]   C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "12637it [03:12, 65.60it/s, +200 | bound: 643 | nc: 1 | ncall: 279584 | eff(%):  4.591 | loglstar:   -inf < 32071.302 <    inf | logz: 32002.278 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 01:33:17,694 - cl_para_pipe_tt5 - INFO - 265000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "12645it [00:20, 28121.47it/s, bound: 644 | nc: 10 | ncall: 279670 | eff(%):  4.521 | loglstar:   -inf < 31529.280 <    inf | logz: 31461.586 +/-    nan | dlogz: 547.212 >  0.209]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "12656it [00:45,  1.14it/s, bound: 645 | nc: 48 | ncall: 280851 | eff(%):  4.506 | loglstar:   -inf < 31536.205 <    inf | logz: 31467.699 +/-    nan | dlogz: 541.582 >  0.209]   C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "12683it [01:50,  2.88s/it, bound: 648 | nc: 10 | ncall: 282629 | eff(%):  4.488 | loglstar:   -inf < 31552.172 <    inf | logz: 31484.440 +/-    nan | dlogz: 677.855 >  0.209]  C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "12690it [02:58, 71.05it/s, +200 | bound: 649 | nc: 1 | ncall: 284591 | eff(%):  4.529 | loglstar:   -inf < 32225.025 <    inf | logz: 32155.737 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 01:37:04,305 - cl_para_pipe_tt5 - INFO - 270000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "12703it [00:12, 35318.84it/s, bound: 649 | nc: 10 | ncall: 284724 | eff(%):  4.462 | loglstar:   -inf < 31563.650 <    inf | logz: 31495.618 +/-    nan | dlogz: 666.676 >  0.209]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "12747it [01:06,  1.94it/s, bound: 651 | nc: 10 | ncall: 286409 | eff(%):  4.451 | loglstar:   -inf < 31589.459 <    inf | logz: 31521.191 +/-    nan | dlogz: 640.989 >  0.209]   C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:216: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "12748it [05:35, 37.95it/s, +200 | bound: 651 | nc: 1 | ncall: 293441 | eff(%):  4.412 | loglstar:   -inf < 32225.025 <    inf | logz: 32155.447 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 01:43:29,062 - cl_para_pipe_tt5 - INFO - 275000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "12754it [00:18, 30472.31it/s, bound: 652 | nc: 10 | ncall: 293501 | eff(%):  4.345 | loglstar:   -inf < 31590.698 <    inf | logz: 31523.165 +/-    nan | dlogz: 638.484 >  0.209]C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "12768it [00:58,  1.89it/s, bound: 653 | nc: 10 | ncall: 294993 | eff(%):  4.328 | loglstar:   -inf < 31599.168 <    inf | logz: 31531.196 +/-    nan | dlogz: 630.552 >  0.209]   C:\\ProgramData\\Anaconda3\\lib\\site-packages\\dynesty\\sampling.py:238: UserWarning: Random walk proposals appear to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random walk proposals appear to be \"\n",
      "12786it [02:59, 71.35it/s, +200 | bound: 654 | nc: 1 | ncall: 298451 | eff(%):  4.351 | loglstar:   -inf < 32225.025 <    inf | logz: 32155.258 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 01:47:17,256 - cl_para_pipe_tt5 - INFO - 280000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "13194it [03:12, 68.67it/s, +200 | bound: 671 | nc: 1 | ncall: 303460 | eff(%):  4.414 | loglstar:   -inf < 32397.408 <    inf | logz: 32325.606 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 01:51:19,012 - cl_para_pipe_tt5 - INFO - 285000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "13610it [03:05, 73.55it/s, +200 | bound: 690 | nc: 1 | ncall: 308464 | eff(%):  4.477 | loglstar:   -inf < 32459.883 <    inf | logz: 32386.006 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 01:55:20,004 - cl_para_pipe_tt5 - INFO - 290000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "14035it [03:09, 74.04it/s, +200 | bound: 706 | nc: 1 | ncall: 313470 | eff(%):  4.541 | loglstar:   -inf < 32661.629 <    inf | logz: 32585.632 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 01:59:17,859 - cl_para_pipe_tt5 - INFO - 295000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "14449it [03:11, 75.63it/s, +200 | bound: 723 | nc: 1 | ncall: 318479 | eff(%):  4.600 | loglstar:   -inf < 32683.948 <    inf | logz: 32605.887 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 02:03:18,514 - cl_para_pipe_tt5 - INFO - 300000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "14867it [03:15, 75.87it/s, +200 | bound: 742 | nc: 1 | ncall: 323490 | eff(%):  4.658 | loglstar:   -inf < 32814.088 <    inf | logz: 32733.942 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 02:07:27,231 - cl_para_pipe_tt5 - INFO - 305000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "15261it [03:14, 78.49it/s, +200 | bound: 760 | nc: 1 | ncall: 328496 | eff(%):  4.707 | loglstar:   -inf < 32832.750 <    inf | logz: 32751.316 +/-    nan | dlogz:  0.701 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 02:11:35,021 - cl_para_pipe_tt5 - INFO - 310000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "15662it [03:14, 80.33it/s, +200 | bound: 776 | nc: 1 | ncall: 333511 | eff(%):  4.756 | loglstar:   -inf < 32901.171 <    inf | logz: 32817.060 +/-    nan | dlogz:  1.098 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 02:15:45,196 - cl_para_pipe_tt5 - INFO - 315000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "16036it [03:14, 82.28it/s, +200 | bound: 793 | nc: 1 | ncall: 338515 | eff(%):  4.796 | loglstar:   -inf < 32968.103 <    inf | logz: 32882.126 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 02:19:53,342 - cl_para_pipe_tt5 - INFO - 320000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "16416it [03:14, 84.30it/s, +200 | bound: 811 | nc: 1 | ncall: 343520 | eff(%):  4.837 | loglstar:   -inf < 32980.091 <    inf | logz: 32893.209 +/-    nan | dlogz:  0.556 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 02:24:09,405 - cl_para_pipe_tt5 - INFO - 325000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "16827it [03:18, 84.74it/s, +200 | bound: 827 | nc: 1 | ncall: 348523 | eff(%):  4.885 | loglstar:   -inf < 33007.222 <    inf | logz: 32918.375 +/-    nan | dlogz:  0.520 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 02:28:25,399 - cl_para_pipe_tt5 - INFO - 330000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "17192it [03:14, 88.33it/s, +200 | bound: 843 | nc: 1 | ncall: 353524 | eff(%):  4.920 | loglstar:   -inf < 33038.144 <    inf | logz: 32947.013 +/-    nan | dlogz:  0.735 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 02:32:36,710 - cl_para_pipe_tt5 - INFO - 335000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "17598it [03:13, 91.08it/s, +200 | bound: 860 | nc: 1 | ncall: 358526 | eff(%):  4.964 | loglstar:   -inf < 33081.324 <    inf | logz: 32987.557 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-15 02:36:48,283 - cl_para_pipe_tt5 - INFO - 340000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "18020it [03:12, 93.53it/s, +200 | bound: 877 | nc: 1 | ncall: 363532 | eff(%):  5.012 | loglstar:   -inf < 33081.324 <    inf | logz: 32985.452 +/-    nan | dlogz:  1.099 >  0.209]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    },
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    },
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     ]
    },
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    },
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     "output_type": "stream",
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    },
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     "text": [
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     ]
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      "2021-12-15 05:36:45,403 - cl_para_pipe_tt5 - INFO - Removing zip file\n"
     ]
    }
   ],
   "source": [
    "'''run the parametric fitting'''\n",
    "###limit the lens and source mnodel to reduce parameters space dimensions\n",
    "lens_galaxy_model = af.Model(al.Galaxy, redshift=redshift_l, mass=al.mp.EllPowerLaw, shear=al.mp.ExternalShear, mass_1=al.mp.EllPowerLaw)\n",
    "source_galaxy_model = af.Model(al.Galaxy, redshift=redshift_s, bulge_1=al.lp.EllSersic, bulge_2=al.lp.SphExponential)\n",
    "\n",
    "lens_galaxy_model.mass.centre_0 = 0.001\n",
    "lens_galaxy_model.mass.centre_1 = -0.003\n",
    "\n",
    "source_galaxy_model.bulge_2.centre_0 = -0.034\n",
    "source_galaxy_model.bulge_2.centre_1 = 0.0165\n",
    "source_galaxy_model.bulge_2.effective_radius=0.0001\n",
    "\n",
    "model = af.Collection(\n",
    "    galaxies=af.Collection(lens=lens_galaxy_model, source=source_galaxy_model)\n",
    ")\n",
    "\n",
    "'''mcmc fitting'''\n",
    "search = af.DynestyStatic(\n",
    "    path_prefix=path.join(\"howtolens\"),\n",
    "    name=testid,\n",
    "    unique_tag=dataname,\n",
    "    nlive=200,\n",
    "    #number_of_cores=8,\n",
    "    walks=10 \n",
    ")\n",
    "\n",
    "settings_lens = al.SettingsLens(positions_threshold=0.5)\n",
    "\n",
    "analysis = al.AnalysisImaging(dataset=imaging, positions=positions, settings_lens=settings_lens)\n",
    "#analysis = al.AnalysisImaging(dataset=imaging)\n",
    "\n",
    "result_1 = search.fit(model=model, analysis=analysis)\n",
    "hyper_image = result_1.model_image.binned.slim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''pass the parametres to the next model'''\n",
    "mass = al.mp.EllPowerLaw(centre=(0.001,-0.003), elliptical_comps=(-0.124, -0.273), einstein_radius=0.638, slope=1.855)\n",
    "shear = al.mp.ExternalShear(elliptical_comps=(0.031, -0.068))\n",
    "pixelization=af.Model(al.pix.VoronoiBrightnessImage)\n",
    "regularization=af.Model(al.reg.Constant)\n",
    "\n",
    "lens_galaxy_model = af.Model(al.Galaxy, redshift=redshift_l, mass=mass, shear=shear)\n",
    "\n",
    "source_reconstruction = af.Model(al.Galaxy, redshift=redshift_s, pixelization=pixelization, regularization=regularization,hyper_image=hyper_image)\n",
    "\n",
    "model = af.Collection(\n",
    "    galaxies=af.Collection(lens=lens_galaxy_model, source=source_reconstruction), \n",
    "    hyper_image_sky=hyper_image,\n",
    "    hyper_background_noise=hyper_image,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "type object 'SetupHyper' has no attribute 'hyper_image_sky'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-3ee1d8f5bccb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m result_1 = al.util.model.hyper_fit(\n\u001b[0m\u001b[1;32m      2\u001b[0m         \u001b[0msetup_hyper\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mal\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSetupHyper\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m         \u001b[0mresult\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresult_1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m         \u001b[0manalysis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0manalysis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[0minclude_hyper_image_sky\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/autogalaxy/analysis/model_util.py\u001b[0m in \u001b[0;36mhyper_fit\u001b[0;34m(setup_hyper, result, analysis, include_hyper_image_sky)\u001b[0m\n\u001b[1;32m    337\u001b[0m         \u001b[0manalysis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_hyper_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    338\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 339\u001b[0;31m     hyper_noise_model = hyper_noise_model_from(\n\u001b[0m\u001b[1;32m    340\u001b[0m         \u001b[0msetup_hyper\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msetup_hyper\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    341\u001b[0m         \u001b[0mresult\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/autogalaxy/analysis/model_util.py\u001b[0m in \u001b[0;36mhyper_noise_model_from\u001b[0;34m(setup_hyper, result, include_hyper_image_sky)\u001b[0m\n\u001b[1;32m    184\u001b[0m     \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclean_model_of_hyper_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 186\u001b[0;31m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhyper_image_sky\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msetup_hyper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhyper_image_sky\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    187\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhyper_background_noise\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msetup_hyper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhyper_background_noise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    188\u001b[0m     '''\n",
      "\u001b[0;31mAttributeError\u001b[0m: type object 'SetupHyper' has no attribute 'hyper_image_sky'"
     ]
    }
   ],
   "source": [
    "'''setups for the next fitting'''\n",
    "settings_lens = al.SettingsLens(\n",
    "    positions_threshold=positions_threshold\n",
    ")\n",
    "\n",
    "analysis = al.AnalysisImaging(\n",
    "    dataset=imaging,\n",
    "    positions=positions,\n",
    "    settings_lens=settings_lens,\n",
    ")\n",
    "\n",
    "result_1 = extensions.hyper_fit(\n",
    "        setup_hyper=al.SetupHyper,\n",
    "        result=result_1,\n",
    "        analysis=analysis,\n",
    "        include_hyper_image_sky=True,\n",
    "    )\n",
    "\n",
    "result_1 = al.util.model.hyper_fit(\n",
    "        setup_hyper=al.SetupHyper,\n",
    "        result=result_1,\n",
    "        analysis=analysis,\n",
    "        include_hyper_image_sky=True,\n",
    "    )\n",
    "\n",
    "#import extensions\n",
    "setup_hyper = al.SetupHyper(\n",
    "    hyper_galaxies_lens=False,\n",
    "    hyper_galaxies_source=True,\n",
    "    hyper_image_sky=al.hyper_data.HyperImageSky,\n",
    "    hyper_background_noise=None,\n",
    ")\n",
    "analysis = al.AnalysisImaging(dataset=imaging)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-01 14:09:32,836 - autofit.non_linear.abstract_search - INFO - Creating search\n",
      "2021-12-01 14:09:32,837 - cl_no0_5__hyper_noise - INFO - Starting search\n",
      "2021-12-01 14:09:34,305 - autolens.imaging.model.analysis - INFO - PRELOADS - Setting up preloads, may take a few minutes for fits using an inversion.\n",
      "2021-12-01 14:09:34,349 - autolens.lens.model.preloads - INFO - PRELOADS - Blurred image (e.g. the image of all light profiles) is preloaded for this model-fit.\n",
      "2021-12-01 14:09:34,471 - autolens.lens.model.preloads - INFO - PRELOADS - Traced grid of planes (for inversion) preloaded for this model-fit.\n",
      "2021-12-01 14:09:34,494 - cl_no0_5__hyper_noise - INFO - Saving path info\n",
      "2021-12-01 14:09:34,616 - cl_no0_5__hyper_noise - INFO - Not complete. Starting non-linear search.\n",
      "2021-12-01 14:09:34,618 - cl_no0_5__hyper_noise - INFO - number_of_cores == 8...\n",
      "2021-12-01 14:09:34,620 - cl_no0_5__hyper_noise - WARNING - ...using SneakyPool. This copies the likelihood functionto each process on instantiation to avoid copying multipletimes.\n",
      "2021-12-01 14:09:34,621 - process 0 - INFO - created\n",
      "2021-12-01 14:09:34,622 - process 1 - INFO - created\n",
      "2021-12-01 14:09:34,623 - process 2 - INFO - created\n",
      "2021-12-01 14:09:34,624 - process 3 - INFO - created\n",
      "2021-12-01 14:09:34,626 - process 4 - INFO - created\n",
      "2021-12-01 14:09:34,627 - process 5 - INFO - created\n",
      "2021-12-01 14:09:34,628 - process 6 - INFO - created\n",
      "2021-12-01 14:09:34,630 - process 7 - INFO - created\n",
      "2021-12-01 14:10:11,946 - autofit.non_linear.initializer - INFO - Generating initial samples of model, which are subject to prior limits and other constraints.\n",
      "2021-12-01 14:10:12,173 - cl_no0_5__hyper_noise - INFO - No Dynesty samples found, beginning new non-linear search. \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "600it [00:14, 40.42it/s, +50 | bound: 39 | nc: 1 | ncall: 5053 | eff(%): 12.864 | loglstar:   -inf < 20763.859 <    inf | logz: 20747.353 +/-  0.810 | dlogz:  1.099 >  0.059]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-01 14:10:27,266 - cl_no0_5__hyper_noise - INFO - 5000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1148it [00:08, 141.74it/s, +50 | bound: 141 | nc: 1 | ncall: 10058 | eff(%): 11.911 | loglstar:   -inf < 32390.240 <    inf | logz: 32363.177 +/-  1.079 | dlogz:  0.912 >  0.059]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-01 14:10:53,358 - cl_no0_5__hyper_noise - INFO - 10000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "1640it [00:07, 209.76it/s, +50 | bound: 230 | nc: 1 | ncall: 15080 | eff(%): 11.207 | loglstar:   -inf < 32405.580 <    inf | logz: 32373.656 +/-  1.087 | dlogz:  0.011 >  0.059]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-01 14:11:19,729 - cl_no0_5__hyper_noise - INFO - 15000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2379it [00:07, 320.87it/s, +50 | bound: 344 | nc: 1 | ncall: 20083 | eff(%): 12.095 | loglstar:   -inf < 32451.559 <    inf | logz: 32404.622 +/-  1.356 | dlogz:  0.016 >  0.059]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-01 14:11:46,609 - cl_no0_5__hyper_noise - INFO - 20000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2523it [00:01, 1748.92it/s, +50 | bound: 366 | nc: 1 | ncall: 21041 | eff(%): 12.229 | loglstar:   -inf < 32451.738 <    inf | logz: 32404.643 +/-  1.338 | dlogz:  0.001 >  0.059]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-01 14:12:08,551 - cl_no0_5__hyper_noise - INFO - 25000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2523it [00:00, 840326.29it/s, +50 | bound: 366 | nc: 1 | ncall: 21041 | eff(%): 12.229 | loglstar:   -inf < 32451.738 <    inf | logz: 32404.643 +/-  1.338 | dlogz:  0.001 >  0.059]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-12-01 14:12:29,324 - cl_no0_5__hyper_noise - INFO - 30000 Iterations: Performing update (Visualization, outputting samples, etc.).\n",
      "2021-12-01 14:12:48,374 - cl_no0_5__hyper_noise - INFO - 35000 Iterations: Performing update (Visualization, outputting samples, etc.).\n",
      "2021-12-01 14:13:16,609 - cl_no0_5__hyper_noise - INFO - Removing zip file\n"
     ]
    }
   ],
   "source": [
    "'''brightness-weighted model; mcmc fitting'''\n",
    "result_2 = al.util.model.hyper_fit(\n",
    "    setup_hyper=setup_hyper,\n",
    "    result=result_1,\n",
    "    analysis=analysis,\n",
    "    include_hyper_image_sky=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "'''pass the parametres to the next model'''\n",
    "lens = af.Model(al.Galaxy,\n",
    "                redshift=redshift_l,\n",
    "                mass=al.mp.EllPowerLaw(\n",
    "                    centre=(0.001,-0.003), elliptical_comps=(-0.124, -0.273), einstein_radius=0.638, slope=1.855\n",
    "                    ),\n",
    "                shear=al.mp.ExternalShear(elliptical_comps=(0.031, -0.068)),)\n",
    "pixelization=al.pix.VoronoiMagnification\n",
    "regularization=al.reg.Constant\n",
    "model = af.Collection(\n",
    "    galaxies=af.Collection(\n",
    "        lens=lens,\n",
    "        source=af.Model(al.Galaxy, redshift=redshift_s, pixelization=pixelization, regularization=regularization),\n",
    "    ),\n",
    "        hyper_image_sky=result_2.hyper.instance.hyper_image_sky,\n",
    "        hyper_background_noise=result_2.hyper.instance.hyper_background_noise,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''setup the next fitting'''\n",
    "analysis = al.AnalysisImaging(dataset=imaging, hyper_dataset_result=result_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-11-26 15:08:02,632 - autofit.non_linear.abstract_search - INFO - Creating search\n",
      "2021-11-26 15:08:02,634 - hyper_t1 - INFO - Starting search\n",
      "2021-11-26 15:08:02,674 - hyper_t1 - INFO - Already completed, skipping non-linear search.\n",
      "2021-11-26 15:08:02,708 - autoarray.dataset.imaging - INFO - IMAGING - Computing W-Tilde... May take a moment.\n",
      "2021-11-26 15:08:09,232 - hyper_t1 - INFO - Removing zip file\n"
     ]
    }
   ],
   "source": [
    "'''mcmc fitting'''\n",
    "search = af.DynestyStatic(\n",
    "    path_prefix=path.join(\"howtolens\"),\n",
    "    name='hyper_t1',\n",
    "    unique_tag=dataname,\n",
    "    nlive=200,\n",
    "    #number_of_cores=8,\n",
    "    walks=10 \n",
    ")\n",
    "\n",
    "result_3 = search.fit(model=model, analysis=analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''lens and source model based on the previous fitting'''\n",
    "lens = af.Model(al.Galaxy,\n",
    "                redshift=redshift_l,\n",
    "                mass=al.mp.EllPowerLaw,\n",
    "                shear=al.mp.ExternalShear)\n",
    "source = af.Model(al.Galaxy, \n",
    "                  redshift=redshift_s, \n",
    "                  pixelization=al.pix.VoronoiBrightnessImage, \n",
    "                  regularization=al.reg.Constant, \n",
    "                  hyper_galaxy=result_3.instance.galaxies.source.hyper_galaxy\n",
    "                 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''pass the parameters priority'''\n",
    "lens.mass.centre_0 = af.GaussianPrior(\n",
    "    mean=0.001, sigma=0.03, lower_limit=-0.03, upper_limit=0.03\n",
    ")\n",
    "lens.mass.centre_1 = af.GaussianPrior(\n",
    "    mean=-0.003, sigma=0.03, lower_limit=-0.03, upper_limit=0.03\n",
    ")\n",
    "lens.mass.elliptical_comps.elliptical_comps_0 = af.GaussianPrior(\n",
    "    mean=-0.1238, sigma=0.01, lower_limit=-0.1285, upper_limit=-0.1187\n",
    ")\n",
    "lens.mass.elliptical_comps.elliptical_comps_1 = af.GaussianPrior(\n",
    "    mean=-0.2731, sigma=0.01, lower_limit=-0.2772, upper_limit=-0.2683\n",
    ")\n",
    "lens.mass.einstein_radius = af.GaussianPrior(\n",
    "    mean=0.6391, sigma=0.01, lower_limit=0.6343, upper_limit=0.6435\n",
    ")\n",
    "lens.mass.slope = af.GaussianPrior(\n",
    "    mean=1.8576, sigma=0.03, lower_limit=1.8418, upper_limit=1.8726\n",
    ")\n",
    "lens.shear.elliptical_comps.elliptical_comps_0 = af.GaussianPrior(\n",
    "    mean=0.0310, sigma=0.01, lower_limit=0.0278, upper_limit=0.0344\n",
    ")\n",
    "lens.shear.elliptical_comps.elliptical_comps_1 = af.GaussianPrior(\n",
    "    mean=-0.0681, sigma=0.01, lower_limit=-0.0711, upper_limit=-0.0650\n",
    ")\n",
    "\n",
    "source.pixelization.pixels = af.GaussianPrior(\n",
    "    mean=3600, sigma=1000, lower_limit=900, upper_limit=4900\n",
    ")\n",
    "source.regularization.inner_coefficient = af.GaussianPrior(\n",
    "    mean=117**0.5, sigma=1000**0.5, lower_limit=10**0.5, upper_limit=1500**0.5\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-11-26 19:06:55,498 - autofit.non_linear.abstract_search - INFO - Creating search\n",
      "2021-11-26 19:06:55,501 - t5 - INFO - Starting search\n",
      "2021-11-26 19:06:56,833 - autolens.imaging.model.analysis - INFO - PRELOADS - Setting up preloads, may take a few minutes for fits using an inversion.\n",
      "2021-11-26 19:07:27,658 - autoarray.preloads - INFO - PRELOADS - Computing W-Tilde... May take a moment.\n",
      "2021-11-26 19:07:30,464 - autoarray.preloads - INFO - PRELOADS - W-Tilde preloaded for this model-fit.\n",
      "2021-11-26 19:07:30,466 - autolens.lens.model.preloads - INFO - PRELOADS - Blurred image (e.g. the image of all light profiles) is preloaded for this model-fit.\n",
      "2021-11-26 19:08:23,036 - t5 - INFO - Saving path info\n",
      "2021-11-26 19:08:23,137 - t5 - INFO - Not complete. Starting non-linear search.\n",
      "2021-11-26 19:08:23,139 - t5 - INFO - number_of_cores == 1...\n",
      "2021-11-26 19:08:23,139 - t5 - INFO - ...not using pool\n",
      "2021-11-26 19:08:23,140 - autofit.non_linear.initializer - INFO - Generating initial samples of model, which are subject to prior limits and other constraints.\n",
      "2021-11-26 19:14:41,601 - t5 - INFO - No Dynesty samples found, beginning new non-linear search. \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/dynesty.py:373: UserWarning: A note of caution: having `nlive < ndim * (ndim + 1) // 2` may result in unconstrained bounding distributions.\n",
      "  warnings.warn(\"A note of caution: \"\n",
      "73it [1:54:48, 413.52s/it, bound: 0 | nc: 40 | ncall: 758 | eff(%):  9.631 | loglstar:   -inf < 33741.310 <    inf | logz: 33734.833 +/-  0.649 | dlogz: 233.303 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "74it [1:55:40, 305.08s/it, bound: 1 | nc: 5 | ncall: 763 | eff(%):  9.699 | loglstar:   -inf < 33750.888 <    inf | logz: 33744.368 +/-  0.654 | dlogz: 228.104 > 10.000] /astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "77it [1:59:12, 150.34s/it, bound: 1 | nc: 5 | ncall: 778 | eff(%):  9.897 | loglstar:   -inf < 33779.854 <    inf | logz: 33773.235 +/-  0.659 | dlogz: 204.191 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "101it [2:25:29, 69.96s/it, bound: 6 | nc: 5 | ncall: 898 | eff(%): 11.247 | loglstar:   -inf < 33879.697 <    inf | logz: 33873.272 +/-  0.640 | dlogz: 146.825 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "114it [2:39:07, 63.88s/it, bound: 9 | nc: 5 | ncall: 963 | eff(%): 11.838 | loglstar:   -inf < 33912.582 <    inf | logz: 33904.750 +/-  0.716 | dlogz: 138.431 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "118it [2:42:20, 53.89s/it, bound: 9 | nc: 5 | ncall: 983 | eff(%): 12.004 | loglstar:   -inf < 33933.442 <    inf | logz: 33925.481 +/-  0.722 | dlogz: 149.350 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "130it [2:53:45, 56.51s/it, bound: 12 | nc: 5 | ncall: 1043 | eff(%): 12.464 | loglstar:   -inf < 33961.738 <    inf | logz: 33953.838 +/-  0.698 | dlogz: 115.782 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "155it [3:21:34, 68.29s/it, bound: 17 | nc: 5 | ncall: 1168 | eff(%): 13.271 | loglstar:   -inf < 33992.614 <    inf | logz: 33984.766 +/-  0.688 | dlogz: 109.536 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "162it [3:31:15, 92.17s/it, bound: 18 | nc: 5 | ncall: 1203 | eff(%): 13.466 | loglstar:   -inf < 34003.425 <    inf | logz: 33994.112 +/-  0.766 | dlogz: 102.060 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "165it [3:34:19, 71.34s/it, bound: 19 | nc: 5 | ncall: 1218 | eff(%): 13.547 | loglstar:   -inf < 34010.522 <    inf | logz: 34001.103 +/-  0.773 | dlogz: 95.118 > 10.000] /astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "171it [3:40:55, 69.86s/it, bound: 20 | nc: 5 | ncall: 1248 | eff(%): 13.702 | loglstar:   -inf < 34013.865 <    inf | logz: 34005.968 +/-  0.702 | dlogz: 87.551 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "174it [3:43:44, 64.09s/it, bound: 21 | nc: 5 | ncall: 1263 | eff(%): 13.777 | loglstar:   -inf < 34019.942 <    inf | logz: 34010.473 +/-  0.757 | dlogz: 84.127 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "190it [3:59:54, 59.04s/it, bound: 24 | nc: 5 | ncall: 1343 | eff(%): 14.147 | loglstar:   -inf < 34037.556 <    inf | logz: 34028.640 +/-  0.731 | dlogz: 64.419 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "194it [4:03:07, 55.01s/it, bound: 25 | nc: 5 | ncall: 1363 | eff(%): 14.233 | loglstar:   -inf < 34039.963 <    inf | logz: 34030.753 +/-  0.730 | dlogz: 62.149 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "200it [4:09:31, 63.93s/it, bound: 26 | nc: 5 | ncall: 1393 | eff(%): 14.358 | loglstar:   -inf < 34045.721 <    inf | logz: 34035.760 +/-  0.767 | dlogz: 57.459 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "206it [4:15:54, 66.75s/it, bound: 27 | nc: 5 | ncall: 1423 | eff(%): 14.476 | loglstar:   -inf < 34048.149 <    inf | logz: 34039.054 +/-  0.743 | dlogz: 53.334 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "214it [4:24:14, 58.74s/it, bound: 29 | nc: 5 | ncall: 1463 | eff(%): 14.627 | loglstar:   -inf < 34053.553 <    inf | logz: 34043.313 +/-  0.774 | dlogz: 49.163 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "218it [4:28:54, 68.13s/it, bound: 29 | nc: 5 | ncall: 1483 | eff(%): 14.700 | loglstar:   -inf < 34062.012 <    inf | logz: 34051.444 +/-  0.803 | dlogz: 41.292 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "227it [4:37:46, 62.30s/it, bound: 31 | nc: 5 | ncall: 1528 | eff(%): 14.856 | loglstar:   -inf < 34066.835 <    inf | logz: 34056.450 +/-  0.787 | dlogz: 35.468 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "248it [5:03:04, 81.93s/it, bound: 35 | nc: 5 | ncall: 1633 | eff(%): 15.187 | loglstar:   -inf < 34076.221 <    inf | logz: 34065.703 +/-  0.803 | dlogz: 36.096 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "256it [5:12:49, 73.73s/it, bound: 37 | nc: 5 | ncall: 1673 | eff(%): 15.302 | loglstar:   -inf < 34080.278 <    inf | logz: 34068.998 +/-  0.811 | dlogz: 32.724 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "264it [5:23:36, 86.85s/it, bound: 39 | nc: 5 | ncall: 1713 | eff(%): 15.412 | loglstar:   -inf < 34082.540 <    inf | logz: 34071.914 +/-  0.806 | dlogz: 29.238 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "266it [5:25:55, 78.31s/it, bound: 39 | nc: 5 | ncall: 1723 | eff(%): 15.438 | loglstar:   -inf < 34083.645 <    inf | logz: 34072.599 +/-  0.810 | dlogz: 28.577 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "275it [5:36:33, 69.00s/it, bound: 41 | nc: 5 | ncall: 1768 | eff(%): 15.554 | loglstar:   -inf < 34086.528 <    inf | logz: 34075.126 +/-  0.817 | dlogz: 26.065 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "280it [5:42:55, 74.01s/it, bound: 42 | nc: 5 | ncall: 1793 | eff(%): 15.616 | loglstar:   -inf < 34087.709 <    inf | logz: 34076.589 +/-  0.820 | dlogz: 24.301 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "282it [5:45:10, 72.08s/it, bound: 42 | nc: 5 | ncall: 1803 | eff(%): 15.641 | loglstar:   -inf < 34088.050 <    inf | logz: 34077.029 +/-  0.818 | dlogz: 23.760 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "290it [5:54:31, 68.13s/it, bound: 44 | nc: 5 | ncall: 1843 | eff(%): 15.735 | loglstar:   -inf < 34091.757 <    inf | logz: 34079.551 +/-  0.848 | dlogz: 21.765 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "294it [5:58:28, 61.54s/it, bound: 45 | nc: 5 | ncall: 1863 | eff(%): 15.781 | loglstar:   -inf < 34093.856 <    inf | logz: 34081.574 +/-  0.864 | dlogz: 23.217 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "303it [6:09:21, 72.59s/it, bound: 46 | nc: 5 | ncall: 1908 | eff(%): 15.881 | loglstar:   -inf < 34096.462 <    inf | logz: 34084.390 +/-  0.852 | dlogz: 21.125 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "307it [6:13:46, 68.16s/it, bound: 47 | nc: 5 | ncall: 1928 | eff(%): 15.923 | loglstar:   -inf < 34098.577 <    inf | logz: 34085.907 +/-  0.868 | dlogz: 19.643 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "321it [6:28:34, 65.99s/it, bound: 50 | nc: 5 | ncall: 1998 | eff(%): 16.066 | loglstar:   -inf < 34103.220 <    inf | logz: 34090.750 +/-  0.875 | dlogz: 15.080 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "323it [6:29:57, 54.03s/it, bound: 50 | nc: 5 | ncall: 2008 | eff(%): 16.086 | loglstar:   -inf < 34103.482 <    inf | logz: 34091.161 +/-  0.871 | dlogz: 16.119 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "330it [6:38:04, 74.26s/it, bound: 52 | nc: 5 | ncall: 2043 | eff(%): 16.153 | loglstar:   -inf < 34105.996 <    inf | logz: 34093.354 +/-  0.882 | dlogz: 13.730 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "340it [6:48:19, 61.24s/it, bound: 54 | nc: 5 | ncall: 2093 | eff(%): 16.245 | loglstar:   -inf < 34108.375 <    inf | logz: 34095.725 +/-  0.883 | dlogz: 10.941 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "349it [6:57:37, 63.80s/it, bound: 56 | nc: 5 | ncall: 2138 | eff(%): 16.324 | loglstar:   -inf < 34108.988 <    inf | logz: 34096.683 +/-  0.873 | dlogz: 17.125 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "353it [7:02:18, 70.43s/it, bound: 56 | nc: 5 | ncall: 2158 | eff(%): 16.358 | loglstar:   -inf < 34109.506 <    inf | logz: 34097.005 +/-  0.871 | dlogz: 16.678 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "359it [7:09:09, 70.31s/it, bound: 58 | nc: 5 | ncall: 2188 | eff(%): 16.408 | loglstar:   -inf < 34110.326 <    inf | logz: 34097.524 +/-  0.873 | dlogz: 15.972 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "361it [7:10:51, 60.78s/it, bound: 58 | nc: 5 | ncall: 2198 | eff(%): 16.424 | loglstar:   -inf < 34110.413 <    inf | logz: 34097.692 +/-  0.873 | dlogz: 15.723 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "368it [7:18:46, 60.90s/it, bound: 59 | nc: 5 | ncall: 2233 | eff(%): 16.480 | loglstar:   -inf < 34111.451 <    inf | logz: 34098.286 +/-  0.878 | dlogz: 15.354 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "369it [7:19:29, 55.50s/it, bound: 60 | nc: 5 | ncall: 2238 | eff(%): 16.488 | loglstar:   -inf < 34111.698 <    inf | logz: 34098.390 +/-  0.880 | dlogz: 15.233 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "373it [7:23:18, 59.58s/it, bound: 60 | nc: 5 | ncall: 2258 | eff(%): 16.519 | loglstar:   -inf < 34112.364 <    inf | logz: 34098.821 +/-  0.887 | dlogz: 16.654 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "384it [7:35:14, 70.18s/it, bound: 63 | nc: 5 | ncall: 2319 | eff(%): 16.559 | loglstar:   -inf < 34113.971 <    inf | logz: 34100.174 +/-  0.909 | dlogz: 14.935 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "386it [7:36:44, 57.92s/it, bound: 63 | nc: 5 | ncall: 2329 | eff(%): 16.574 | loglstar:   -inf < 34114.638 <    inf | logz: 34100.446 +/-  0.913 | dlogz: 14.631 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "393it [7:45:28, 69.47s/it, bound: 65 | nc: 5 | ncall: 2372 | eff(%): 16.568 | loglstar:   -inf < 34116.864 <    inf | logz: 34102.172 +/-  0.946 | dlogz: 12.748 > 10.000] /astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "402it [7:54:52, 64.45s/it, bound: 67 | nc: 5 | ncall: 2421 | eff(%): 16.605 | loglstar:   -inf < 34117.961 <    inf | logz: 34103.572 +/-  0.944 | dlogz: 14.413 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "412it [8:05:43, 61.07s/it, bound: 69 | nc: 5 | ncall: 2471 | eff(%): 16.673 | loglstar:   -inf < 34119.555 <    inf | logz: 34104.755 +/-  0.946 | dlogz: 12.909 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "413it [8:06:33, 57.55s/it, bound: 69 | nc: 5 | ncall: 2476 | eff(%): 16.680 | loglstar:   -inf < 34119.589 <    inf | logz: 34104.867 +/-  0.946 | dlogz: 12.751 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "414it [8:07:40, 60.56s/it, bound: 70 | nc: 5 | ncall: 2481 | eff(%): 16.687 | loglstar:   -inf < 34119.846 <    inf | logz: 34104.981 +/-  0.947 | dlogz: 12.606 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "419it [8:12:17, 58.05s/it, bound: 71 | nc: 5 | ncall: 2508 | eff(%): 16.707 | loglstar:   -inf < 34120.456 <    inf | logz: 34105.559 +/-  0.951 | dlogz: 11.851 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "425it [8:18:39, 65.05s/it, bound: 72 | nc: 5 | ncall: 2538 | eff(%): 16.745 | loglstar:   -inf < 34120.972 <    inf | logz: 34106.114 +/-  0.952 | dlogz: 11.086 > 10.000]/astrosoft/anaconda/anaconda3/lib/python3.8/site-packages/dynesty/sampling.py:371: UserWarning: Random number generation appears to be extremely inefficient. Adjusting the scale-factor accordingly.\n",
      "  warnings.warn(\"Random number generation appears to be \"\n",
      "434it [8:27:54, 70.22s/it, +30 | bound: 74 | nc: 1 | ncall: 2583 | eff(%): 17.964 | loglstar:   -inf < 34131.015 <    inf | logz: 34113.327 +/-  1.650 | dlogz:  0.705 > 10.000]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-11-27 03:42:36,933 - t5 - INFO - 5000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "434it [00:00, 133045.46it/s, +30 | bound: 74 | nc: 1 | ncall: 2583 | eff(%): 17.964 | loglstar:   -inf < 34131.015 <    inf | logz: 34113.327 +/-  1.650 | dlogz:  0.705 > 10.000]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-11-27 03:44:24,162 - t5 - INFO - 10000 Iterations: Performing update (Visualization, outputting samples, etc.).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-11-27 03:46:01,446 - t5 - INFO - 15000 Iterations: Performing update (Visualization, outputting samples, etc.).\n",
      "2021-11-27 03:48:06,400 - t5 - INFO - Removing zip file\n"
     ]
    }
   ],
   "source": [
    "'''run the fitting'''\n",
    "model_t5 = af.Collection(\n",
    "    galaxies=af.Collection(\n",
    "        lens=lens,\n",
    "        source=source,\n",
    "    ),\n",
    "        hyper_image_sky=result_3.instance.hyper_image_sky,\n",
    "        hyper_background_noise=result_3.instance.hyper_background_noise,\n",
    ")\n",
    "analysis_t5 = al.AnalysisImaging(dataset=imaging, \n",
    "                                 positions=positions, settings_lens=settings_lens, \n",
    "                                 hyper_dataset_result=result_3)\n",
    "search = af.DynestyStatic(\n",
    "    path_prefix=path_prefix,\n",
    "    name=\"t5\",\n",
    "    unique_tag=dataset_name,\n",
    "    nlive=30,\n",
    "    dlogz=10.0,\n",
    "    sample=\"rstagger\",\n",
    ")\n",
    "\n",
    "result_t5 = search.fit(model=model_t5, analysis=analysis_t5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''show results'''\n",
    "include_2d = aplt.Include2D(\n",
    "                            mask=True, mapper_data_pixelization_grid=False, mapper_source_pixelization_grid=False, critical_curves=True, caustics=True,\n",
    "                            )\n",
    "fit_imaging_plotter = aplt.FitImagingPlotter(fit=result_t5.max_log_likelihood_fit, include_2d=include_2d)\n",
    "fit_imaging_plotter.figures_2d_of_planes(plane_image=True, plane_index=1)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
